Systems, devices and methods using phase-amplitude coupling measures in implantable medical devices

ABSTRACT

A sensor of an implantable medical device senses electrical activity of the brain. A data analyzer of the device monitors an electrographic signal corresponding to the electrical activity of the sensed brain signal, and processes the brain signal to obtain a measure of phase-amplitude coupling. For a selected portion of the electrographic signal, the data analyzer detects first features and second features of the electrographic signal. The first features represent oscillations in a low frequency range, while the second features represent oscillations in a frequency range higher than the low frequency range. For example, the low frequency range may correspond to theta frequency and the higher frequency range may correspond to gamma frequency. The data analyzer determines a measure of phase-amplitude coupling between oscillations in the low frequency range and oscillations in the higher frequency range based on occurrences of second features which coincide with first features.

BACKGROUND Technical Field

The present disclosure relates to systems, devices, and methods forprocessing neuronal signals, and more particularly to systems includingor devices comprising active implantable medical devices, and methodsperformed thereby, for computing measures of phase-amplitude couplingthrough analyses of intracranially-sensed brain signals.

Background

Neuronal oscillations of different frequencies can interact with oneanother. The interaction of oscillations in different frequency bands iscommonly referred to as “cross-frequency coupling”. In one type ofcross-frequency coupling, known as “phase-amplitude coupling,” theamplitude of high frequency oscillations is modulated by the phase oflow-frequency oscillation. An example of phase-amplitude coupling isevident in certain neuronal oscillations of the hippocampus in thebrain, where the phase of low frequency theta brain oscillations (where“low frequency” is between approximately 4 to 8 Hz), modulates theamplitude of high frequency gamma brain oscillations (where “highfrequency” is generally greater than 40 Hz).

Several methods are known for assessing phase-amplitude coupling. In onetechnique described by Tort et al. in Measuring Phase-Amplitude CouplingBetween Neuronal Oscillations of Different Frequencies, J Neurophysiol104: 1195-1210, May 12, 2010, a sensed brain signal is filtered into twodifferent frequency ranges, to obtain a low frequency brain signal and ahigh frequency brain signal. Using the standard Hilbert transform, atime series of phases is extracted from each of the low frequency brainsignal and the high frequency brain signal. A composite time series isthen constructed, which gives the amplitude of high frequencyoscillations at each phase of the low frequency oscillations. Amodulation index is derived from the composite time series. The indexprovides a measure of phase-amplitude coupling.

This technique of measuring phase-amplitude coupling, along with otherexisting techniques, can be computationally intensive, and generallyrequires the processing capability of an external computer (as opposedto being practical to carry out in a device implanted in a patient).(Processing performed on an external computer may be referred to hereinas “offline” processing.) For reasons described more fully below, it maybe desirable to measure phase-amplitude coupling in an implanted medicaldevice. (Processing by an implantable medical device may be referred toherein as “online” processing.) Processing of data at the time it isbeing sensed, whether the processing is by an external computer or animplanted computer, may be referred to herein as “real time” processing.While technology continues to advance with respect to the potentialcomputing power of implantable devices, and alternatives to a limited,on-device power source are the subject of research, designconsiderations for implanted devices still often limit the degree towhich computationally intensive signal processing can be carried out inan implant (e.g., there are trade-offs between the power, memory andother resources required for the signal processing and other functionsthe implant is meant to perform).

Implantable medical devices are known that use algorithms of relativelylow computational complexity to analyze activity within brain signals,and to determine when certain activity (e.g., patterns) should be deemedto have been detected by the device. One such algorithm involvesidentifying half waves in sensed brain signals that have been signalconditioned and otherwise processed by the implantable medical device.Half wave detection is a way of approximating the power of a signal indifferent frequency bands that is less computationally intensive thanother methods of measuring the frequency content of a signal, such asFast Fourier Transforms (FFTs) and Hilbert Transforms.

U.S. Pat. No. 6,810,285 to Pless et al. for “Seizure Sensing andDetection Using an Implantable Device” describes a half wave detector(also sometimes referred to as a half wave detection tool), for animplanted device (e.g., a neurostimulator). The implanted device can beconfigured so that the half wave detector is used alone or incombination with other forms of data analysis to decide whether sometype of pre-defined neurological event has occurred. U.S. PatentPublication No. 2014/0276181 to Sun et al. for “Methods and Systems forAutomatically Identifying Detection Parameters for an ImplantableMedical Device” describes parameter sets for programming half wavedetectors so that they can be tuned to identify the events of interestwhen they occur in the signals being monitored. U.S. Patent PublicationNo. 2017/0049351 to Rosana Esteller for “Neurological Event DetectionTools for Implantable Medical Devices” also describes various parametersets for programming half wave detectors. Each of these patent documentsis incorporated herein in its entirety by reference.

To date, algorithms of a low enough level of computational complexity tobe practically implemented in an implantable medical device have notbeen applied to measure phase-amplitude coupling. Embodiments disclosedherein are directed to computing measures of phase-amplitude coupling,entirely or in significant part, in an implanted medical device. Thesemeasures may be computed by the implantable medical device in “realtime” on brain signals being sensed by the device, or “online” by thedevice on records of brain signals previously sensed by the device andstored in device memory, or “offline” by an external device that obtainsrecords of brain signals from the device memory. Also disclosed hereinare various beneficial applications of such measures of phase-amplitudecoupling with respect to certain neurological conditions or disorders,and their related brain states.

SUMMARY

In one embodiment, at least one sensor of an active implanted medicaldevice senses electrical activity of the brain. A sensor may compriseone or more electrodes configured to sense field potential measurementscorresponding to the electrical activity of a group of neurons. Thedevice conditions and otherwise processes the sensed electrical activityto produce a digital representation of it—an electrographic signal(alternatively referred to herein as a brain signal or a waveform). Thedevice can be configured to monitor electrical activity sensed frommultiple sensors on one or more sensing channels. For example, thedevice can be configured so that the input from one sensor correspondsto a signal channel, or so that the input from one sensor corresponds tomore than one channel, or so that the combined input from more than onesensor corresponds to a single channel.

A data analyzer of the device monitors the electrographic signal(s), andprocesses the electrographic signal(s) to obtain a measure ofphase-amplitude coupling. The monitoring can be continuous, orsubstantially continuous, or accomplished according to a schedule ortriggered by an event or events. The data analyzer is configured to, fora selected region of interest of each electrographic signal, detectdifferent features. For example, the data analyzer may be configured tolook for first features and second features in the region of interest.The first features may represent the content (or power) of the signal inone frequency range, while the second features may represent the content(or power) of the signal in a different frequency range. The frequenciesin the second frequency range may be higher than those in the firstfrequency range. Analysis of these features may provide insight into themodulation of the amplitude of high frequency oscillations by the phaseof low frequency oscillations, as described more fully herein.

For example, the first frequency range may correspond to frequencies ina low frequency range, such as the theta range (generally between about4 Hz and 8 Hz) and the second frequency range may correspond tofrequencies in a higher frequency range, such as the gamma range(generally above about 40 Hz). The first features and second featuresmay correspond to the occurrence of one or more half waves in the signalthat meet certain pre-determined criteria. To determine whether and, ifso, when the pre-determined criteria are met, a first half wave detectoror half wave tool is configured with parameter values that are tuned toidentify first features, e.g., low-frequency features, based onpre-determined criteria, and a second half wave detector is configuredwith parameter values that are tuned to identify second features, e.g.,higher-frequency features, based on pre-determined criteria. The dataanalyzer determines a measure of phase-amplitude coupling between thefirst features, e.g., the content of the electrographic signal in theregion of interest in the first frequency range (such as theta), and thesecond features, e.g., the content of the electrographic signal in theregion of interest in the second frequency range (such as gamma), basedon where—and to what extent—the first features coincide with the secondfeatures.

Where the two features represent content of the signal in two differentfrequency ranges, determining phase-amplitude coupling between the tworanges may involve: (1) dividing the features (e.g., half waves) withinthe first or low frequency range into one or more portions, phases orphase bins, (2) assigning a designation (e.g., a phase range or phasebin number) to each portion, phase, or phase bin, (3) determining anindividual metric for each portion, phase or phase bin corresponding toa measure (e.g. count) of features (e.g., half waves) within the secondfrequency range that coincides with the portion, phase or phase bin, and(4) aggregating the individual metrics on a per-phase-range orper-bin-number basis to obtain an aggregate metric that correlates to adegree (or a strength) of the phase-amplitude coupling between the twodifferent frequency ranges for each assigned phase range or phase binnumber. The aggregate metric may be a sum or statistical measure ofindividual metrics. A second aggregate metric is then computed acrossthe different phase ranges or phase bin numbers of the low frequencyrange to derive a measure of the phase-amplitude coupling in the regionof interest of the electrographic signal. This second aggregate metriccorresponds to a measure of phase-amplitude coupling or a PAC score andprovides an indication of the extent to which the amplitude of thehigher frequency range in the electrographic signal has a preference tooccur at selective phase(s) of the low frequency range in theelectrographic signal.

It is understood that other aspects of apparatuses and methods willbecome readily apparent to those skilled in the art from the followingdetailed description, wherein various aspects of apparatuses and methodsare shown and described by way of illustration. As will be realized,these aspects may be implemented in other and different forms.Accordingly, the drawings and detailed description are to be regarded asillustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

Various aspects of apparatuses and methods will now be presented in thedetailed description by way of example, and not by way of limitation,with reference to the accompanying drawings, wherein:

FIG. 1 is a perspective, schematic view of a patient's cranium in whichimplantable components are implanted, including an active medical device(e.g. a neurostimulator) and leads extending in or on the patient'sbrain configured to sense electrical activity from the patient's brainso that the activity can be monitored by the medical device.

FIG. 2 is a block diagram of the active medical device (e.g. aneurostimulator) and leads of FIG. 1, illustrating some of thefunctional subsystems of the active medical device.

FIG. 3 is a block diagram of the detection subsystem of FIG. 2,illustrating some of its functional components.

FIG. 4A illustrates a composite electrographic signal including ahigh-frequency component with signal content or oscillation in ahigh-frequency range, and a low-frequency component with signal contentor oscillations in a low-frequency range.

FIG. 4B illustrates the high-frequency component of the electrographicsignal of FIG. 4A.

FIG. 4C illustrates the low-frequency component of the electrographicsignal of FIG. 4A.

FIG. 4D illustrates occurrences of features (e.g., half wave peaks)representing signal content or oscillation in the high-frequencycomponent of the electrographic signal of FIG. 4B.

FIG. 4E illustrates a method of analyzing a region of interest of ahigh-frequency component of an electrographic signal to detect signalcontent or oscillations (e.g., half waves) in a high-frequency range.

FIG. 4F illustrates a method of analyzing a region of interest of alow-frequency component of an electrographic signal to detect signalcontent or oscillations (e.g., half waves) in a low-frequency range, andto divide the detected signal content into designated phase ranges orphase bins.

FIGS. 4G and 4H illustrate histograms representing counts of detectedhigh-frequency content (e.g., half waves) per low-frequency phase rangeor phase bin assignments.

FIG. 5 is a flowchart of a method of measuring phase-amplitude couplingusing an active implantable medical device.

FIGS. 6A-6D illustrate a composite electrographic signal (FIG. 6A) andelectrographic signals derived from the composite, namely, atheta-filtered electrographic signal (FIG. 6B), a gamma-filteredelectrographic signal (FIG. 6C), and an electrographic signal comprisingthe coupling of the theta and gamma waves (FIG. 6D).

FIG. 7A-7B illustrate a method of analyzing a region of interest of anelectrographic signal characterized by cross-frequency coupling, inwhich signal content or oscillations in a first frequency range (e.g.low-frequency, theta) is identified (FIG. 7A) and signal content oroscillations in a second frequency range (e.g., higher frequency, gamma)is identified (FIG. 7B).

FIG. 8 illustrates a method of analyzing a region of interest of anelectrographic signal characterized by cross-frequency coupling in whichthe phase ranges of low-frequency signal content or oscillations areidentified.

FIGS. 9A-9C illustrate a method of analyzing a region of interest of anelectrographic signal characterized by cross-frequency coupling, inwhich low-frequency signal content or oscillations (e.g., half waves)are divided into phase ranges or phase bins, and associated with anumber of data samples of the electrographic signal.

FIG. 10 illustrates a method of analyzing a region of interest of anelectrographic signal characterized by cross-frequency coupling, inwhich cross frequency coupling of oscillations in a low-frequency range(theta) and oscillations in a higher frequency range (gamma) is present.

FIG. 11 illustrates another method of analyzing a region of interest ofan electrographic signal characterized by cross-frequency coupling, inwhich cross frequency coupling of oscillations in a low-frequency range(theta) and oscillations in a higher frequency range (gamma) is present.

FIG. 12 is a block diagram of the therapy subsystem of FIG. 3,illustrating some of its functional components.

FIG. 13A-13G are illustrations of various waveforms representingdifferent electrical stimulation strategies for effecting changes inmeasures of phase-amplitude coupling.

FIG. 14 is a flowchart of a method of delivering neuromodulation therapybased on measures of phase-amplitude coupling.

DETAILED DESCRIPTION

Disclosed herein in detail is a method of calculating measures ofphase-amplitude coupling in an active implantable medical device. Themethod analyzes electrographic signals in the time domain to approximatethe power of the signal in a low frequency range and a higher frequencyrange by detecting oscillations in the electrographic signal in the lowand higher frequency ranges. Once features in the low and higherfrequency ranges are detected, the active implantable medical deviceprocesses information corresponding to the respective features to assignone or more phase ranges to the low frequency features, and to determineone or more metrics, e.g., individual counts and aggregate counts, withrespect to the higher frequency features. The one or more metrics arefurther processed to compute a degree or strength of phase-amplitudecoupling.

Prior to describing the foregoing method of computing measures ofphase-amplitude coupling in an active implantable medical device indetail, an overview of phase-amplitude coupling in electrographicsignals is provided, followed by an overview of an active implantablemedical device that may be configured to implement the method.

Overview of Phase-Amplitude Coupling in Electrographic Signals

As used herein, the term “phase-amplitude coupling” refers to a measurederived from electrographic signals sensed from a subject's neuraltissue in the brain that indicates whether and, if so, to what degreethe phase of lower frequency components (or oscillations) of the signalmodulate the amplitude of higher frequency components (or oscillations).The frequency components of the signal may be conveniently categorizedin ranges, such as the following ranges: delta (approximately 1 to 4Hz), theta (approximately 4 to 8 Hz), alpha (approximately 8 to 13 Hz),and beta (approximately 13 to 30 Hz), gamma (approximately 30 Hz to 80Hz), and high gamma (greater than about 80 Hz).

As used herein the term “electrographic signal” refers to a signal thatrepresents aggregate neuronal activity potentials (local fieldpotentials or LFPs) detectable via electrodes. When the electrodes areapplied to a patient's scalp, the signals acquired are usually referredto as an EEG. When the electrodes are applied intracranially, such asplaced on or near the surface of the brain (e.g., on or near the duramater) or within the brain (e.g., via depth electrodes), the signalsacquired may be referred to as an ECoG (electrocorticogram) or ECoGs(electrocorticographic signals). Electrographic signals, EEG, and ECoGsmay be referred to herein as brain signals or waveforms.

Studies have shown that phase-amplitude coupling serves important brainfunctions, for example, the presence or lack of phase-amplitude couplingis relevant to the brain's cognitive processing ability. R. T. Canoltyand R. T. Knight. The functional role of cross-frequency coupling.Trends Cogn Sci 14 (11):506-515, 2010. It has been proposed thattheta-gamma coupling electrographic signals is a mechanism by whichinformation processing is coordinated across multiple spatiotemporalscales in the brain. R. T. Canolty and R. T. Knight. The functional roleof cross-frequency coupling. Trends Cogn Sci 14 (11):506-515, 2010.

The degree (or strength) of phase-amplitude coupling may be correlatedto a clinical state of the subject, or to a condition or disorder of thesubject. For example, research suggests that the degree ofphase-amplitude coupling between theta and gamma oscillations in sensedbrain signals correlates with a subject's performance accuracy in memorytasks. A. B. Tort, R. W. Komorowski, J. R. Munns, N. J. Kopell, and H.Eichenbaum. Theta-gamma coupling increases during the learning ofitem-context associations. Proc Natl. Acad Sci U.S.A. 106(49):20942-20947, 2009. In addition, in epilepsy, it is known thatphase-amplitude coupling between the phase of delta activity (1-4 Hz)and the amplitude of high-frequency activity (40-140 Hz) increasesaround the time of the onset of a seizure. C. varado-Rojas, M.Valderrama, A. Fouad-Ahmed, H. Feldwisch-Drentrup, M. Isle, C. A.Teixeira, F. Sales, A. Schulze-Bondages, C. Adam, A. Dour ado, S.Charier, V. Navarro, and Quyen M. Le Van. Slow modulations ofhigh-frequency activity (40-140-Hz) discriminate preictal changes inhuman focal epilepsy. Sci Rep 4:4545, 2014].

In other studies, electrical stimulation has successfully been appliedto evoke cross frequency coupling in electrographic signals (neuronaloscillations) where it is absent or not present to a significant degree,with results shown to be beneficial in memory retrieval in rodents. P.R. Shirak, P. R. Rapp, and M. L. Shapiro. Bidirectional changes tohippocampal theta-gamma comodulation predict memory for recent spatialepisodes. Proc Natl. Acad Sci U.S.A. 107 (15):7054-7059, 2010.

Phase-amplitude coupling between theta and gamma components of anelectrographic signal is presently a commonly used measure in research.This measure represents how the phase of the lower frequency component(theta approximately 4 to 8 Hz) modulates the amplitude of the higherfrequency component (gamma approximately 40 Hz to 80 Hz. A. B. Tort, R.W. Komorowski, J. R. Manns, N. J. Kopell, and H. Eichenbaum. Theta-gammacoupling increases during the learning of item-context associations.Proc Natl. Acad Sci U.S.A. 106 (49):20942-20947, 2009; R. T. Canolty andR. T. Knight. The functional role of cross-frequency coupling. TrendsCogn Sci 14 (11):506-515, 2010. This form of phase-amplitude couplingmay be referred to as “theta-gamma coupling” for short.

Several studies have shown that phase-amplitude coupling between thebeta (approximately 13 to 30 Hz) and gamma components of anelectrographic signal may be relevant in patients with the movementdisorder Parkinson's disease (“beta-gamma coupling”). Hemptinne C. de,E. S. Ryapolova-Webb, E. L. Air, P. A. Garcia, K. J. Miller, J. G.Ojemann, J. L. Ostrem, N. B. Galifianakis, and P. A. Starr. Exaggeratedphase-amplitude coupling in the primary motor cortex in Parkinsondisease. Proc Natl. Acad Sci U.S.A. 110 (12):4780-4785, 2013.

Other examples of phase-amplitude coupling include “alpha-gammacoupling”, where the phase of alpha (approximately 8 to 13 Hz) ismeasured with respect to the amplitude of gamma and “alpha-gammacoupling”, where the phase of alpha is measured with respect to theamplitude of gamma (greater than about 40 Hz). F. Roux and P. J.Uhlhaas. Working memory and neural oscillations: alpha-gamma versustheta-gamma codes for distinct WM information? Trends Cogn Sci 18(1):16-25, 2014; and “delta-beta coupling”, where the phase of delta(approximately 1 to 4 Hz) is measured with respect to the amplitude ofthe beta. N. Axmacher, M. M. Henseler, O. Jensen, I. Weinreich, C. E.Elger, and J. Fell. Cross-frequency coupling supports multi-item workingmemory in the human hippocampus. Proc Natl. Acad Sci U.S.A. 107(7):3228-3233, 2010.

Because it is currently a commonly sought measure, aspects of thefollowing detailed description of methods for computing measures ofphase-amplitude coupling in an active implantable medical deviceconcerns theta-gamma coupling, it will be appreciated that the methodsdescribed may be extended to other types of cross-frequency couplingmeasurement, such as those identified above.

If phase-amplitude coupling in electrographic signals can be measured inreal time or close to real time from a subject, the measure may bebeneficially used in diagnosing a condition or disorder of a patient, oras an indicator of a state the patient's brain is in (e.g., a state thatis prone to seizures, a state in which memory performance is optimized,etc.) Moreover, real time measurement of phase-amplitude coupling may beeffective in driving therapeutic interventions (e.g., deliveringelectrical stimulation to reduce the severity of a seizure or to preventone from developing at all) or sustaining a level of or evoking a levelof cross frequency coupling that is deemed beneficial (e.g., to minimizea symptom of a movement disorder, such as tremor).

Real time measurements of phase-amplitude coupling desirably may beaccomplished with one or more implantable components of a medical devicesystem, so that the subject can remain ambulatory and not tied toexternal devices or components while the measurements are beingundertaken. To minimize power requirements of the implantablecomponents, and in light of other trade-offs important and common in thedesign of in implantable medical devices, the measurements beneficiallymay be carried out using an algorithm or algorithms of relatively lowcomputational complexity.

Thus, disclosed herein is a method of calculating measures ofphase-amplitude coupling in an active implantable medical device. Themodifier “active” is used herein for convenience in this description todistinguish the implanted component of medical device system which iscarrying out the calculations from other implanted components of thesystem, such as the leads that are conduits through which electricalactivity sensed from the patient's brain is introduced to the implantedcomponent carrying out the calculations. It will be appreciated, howeverthat a given medical device system incorporating a method of measuringphase-amplitude coupling may include other implantable components thatcontain “active” components inasmuch as they may include and use activeelectronics to acquire measurements (e.g., an active lead) or to delivera therapy (e.g., a separate implantable system component configured todeliver electrical stimulation or some other form of treatment intendedto modulate neural function in the brain).

The method analyzes electrographic signals in the time domain toapproximate the power of the signal in a low frequency range and ahigher frequency range by detecting features, e.g., oscillations, in theelectrographic signal in the low and higher frequency ranges using ahalf wave detector or half wave detection tool. For example, theimplantable medical device may include two half wave detectors, onetuned for the low frequency and the other tuned for the higherfrequency, where each detector is programmed to detect half waves thatsatisfy certain amplitude, duration, and hysteresis criteria indicativeof the low or higher frequency range, respectively. When the method isused to measure theta-gamma coupling, the low frequency half wavedetector may be tuned to detect half waves corresponding to the 4 to 8Hz range, and the higher frequency half wave detector may be tuned todetect half waves corresponding to 40 Hz and above.

Once the half wave detectors have detected features in the low andhigher frequency ranges, the active implantable medical device processesinformation corresponding to the respective features to assign one ormore phase ranges to the low frequency feature, and to determine one ormore metrics, e.g., individual counts and aggregate counts, with respectto the higher frequency features. The one or more metrics are furtherprocessed to compute a degree or strength of phase-amplitude coupling.

Overview of the Active Implantable Medical Device

Embodiments of an active implantable medical device that can beconfigured to implement the method, and a system including it, are nowdescribed with references to FIGS. 1-4.

FIG. 1 is an illustration of the implantable components of a medicaldevice system according to embodiments, namely, an active implantableneurostimulator 110 and two electrode-bearing brain leads 124, 126,implanted in a patient. The neurostimulator 110 is affixed in thepatient's cranium 112 by way of a ferrule 118. The ferrule 118 is astructural member adapted to fit into a cranial opening, attach to thecranium 112, and retain the neurostimulator 110. To implant theneurostimulator 110, a craniotomy is performed in the parietal boneanterior to the lambdoidal suture to define an opening 120 slightlylarger than the neurostimulator 110. The ferrule 118 is inserted intothe opening 120 and affixed to the cranium 112, ensuring a tight andsecure fit. The neurostimulator 110 is then inserted into and affixed tothe ferrule 118.

The neurostimulator 110 includes a lead connector 122 adapted to receiveone or more of the brain leads, such as a deep brain or depth lead 124and a cortical strip lead 126. (The depth lead is intended to beimplanted so that a distal end of it is situated within the patient'sneural tissue, whereas the cortical strip lead is intended to beimplanted under the dura mater so that a distal end of it rests on asurface of the brain). The lead connector 122 acts to physically securethe brain leads 124, 126 to the neurostimulator 110, and facilitateselectrical connection to conductors in the brain leads 124, 126 couplingone or more electrodes at or near a distal end of the lead to circuitrywithin the neurostimulator 110. The lead connector 122 accomplishes thisin a substantially fluid-tight environment with biocompatible materials.

More particularly, the brain leads 124, 126 include a flexible elongatedmember having one or more conductors. As shown, the brain leads 124, 126are coupled to the neurostimulator 110 via the lead connector 122. Theproximal portion of the deep brain lead 124 is generally situated on theouter surface of the cranium 112 (and under the patient's scalp), andextends between the neurostimulator 110 and a burr hole 134 or othercranial opening. The distal portion of the deep brain lead 124 entersthe cranium 112 and is coupled to at least one depth electrode 130implanted in a desired location in the patient's brain. The proximalportion of the cortical lead 126 is generally situated on the outersurface of the cranium 112 (and under the patient's scalp), and extendsbetween the neurostimulator 110 and a burr hole (not visible) or othercranial opening. The distal portion of the cortical lead 126 enters thecranium 112 through the burr hole and is secured in place by a burr holecover 132. The distal portion of the cortical lead 126 includes at leastone cortical electrode (not visible) implanted in a desired location onthe patient's brain.

The neurostimulator 110 includes a durable housing 128 fabricated from abiocompatible material, such as titanium. As the neurostimulator 110 isself-contained, the housing 128 encloses a battery and any electroniccircuitry necessary or desirable to provide the functionality describedherein, as well as any other features. A telemetry coil or other antennamay be provided outside of the housing 128 (and potentially integratedwith the lead connector 122) to facilitate communication between theneurostimulator 110 and external devices.

FIG. 2 is a block diagram of a medical device system including an activeimplantable neurostimulator 110 and two brain leads 124, 126, eachbearing four electrodes 212 a-d, 214 a-d. The neurostimulator 110 may beconfigured to compute measures of phase-amplitude coupling in accordancewith the techniques disclosed herein. The neurostimulator 110 may befurther configured to process the measures of phase-amplitude couplingfor purposes of driving therapeutic interventions in accordance withtechniques disclosed herein.

The neurostimulator 110 includes a lead connector 122 adapted to receivea connector end of each brain lead 124, 126, to thereby electricallycouple each lead and its associated electrodes 212 a-d, 214 a-d with theneurostimulator. The neurostimulator 110 may configure an electrode 212a-d, 214 a-d as either a sensor (for purposes of sensing electricalactivity of the brain, which activity is subsequently processed by othercomponents of the neuro stimulator for purposes of computing measures ofphase-amplitude coupling) or a stimulator (for purposes of deliveringtherapy to the patient in the form of electrical stimulation, whichdelivery may be in response to computed measures of phase-amplitudecoupling) or both. Although eight electrodes 212 a-d, 214 a-d are shownin FIG. 2, more electrodes may be available depending on the number ofimplanted leads and the number of electrodes per lead.

The electrodes 212 a-d, 214 a-d are connected to an electrode interface220. The electrode interface 220 is capable of selecting each electrode212 a-d, 214 a-d as required for sensing and stimulation. The electrodeinterface 220 may also provide any other features, capabilities, oraspects, including but not limited to amplification, isolation, andcharge-balancing functions, that are required for a proper interfacewith neurological tissue. The electrode interface 220 is coupled to adetection subsystem 226, which is configured to process electricalactivity of the brain sensed through the electrode 212 a-d, 214 a-d tocompute measures of phase-amplitude coupling. Details of the detectionsubsystem 226 are described later below with references to FIG. 3. Theelectrode interface 220 may also be coupled to a therapy subsystem 228,which is configured to deliver therapy to the patient through theelectrode 212 a-d, 214 a-d in the form of electrical stimulation.

The neurostimulator 110 includes a memory subsystem 238 and a centralprocessing unit (CPU) 240, which can take the form of a microcontroller.The memory subsystem 238 is coupled to the detection subsystem 226, andmay receive and store data representative of sensed electrographicsignals, measures of phase-amplitude coupling, and other sensor data.The memory subsystem 238 is also coupled to the therapy subsystem 228and the CPU 240. In addition to the memory subsystem 238, the CPU 240 isalso connected to the detection subsystem 226 and the therapy subsystem228 for direct control of those subsystems.

The neurostimulator 110 also includes a communication subsystem 242. Thecommunication subsystem 242 enables communication between theneurostimulator 110 and the outside world, such as an externalprogrammer, through a wireless communication link. The programmer allowsthe physician to read out a history of events detected includingelectrographic signal information before, during, and after eachneurological event, as well as specific information relating to thedetection of each event. Information related to measures ofphase-amplitude coupling may also be read from the neurostimulator 110.The neurostimulator 110 also includes a power supply 244 and a clocksupply 246. The power supply 244 supplies the voltages and currentsnecessary for each of the other subsystems. The clock supply 246supplies substantially all the other subsystems with any clock andtiming signals necessary for their operation.

FIG. 3 illustrates details of the detection subsystem 226 of FIG. 2.Signals received from the electrodes 212 a-d, 214 a-d are received in anelectrode selector 310. The electrode selector 310 allows the device toselect which electrodes 212 a-d, 214 a-d should be routed to whichindividual sensing channels 313 a, 313 b, 313 c associated with thesensing front end 312.

The electrode selector 310 provides signals corresponding to each pairof selected electrodes to the sensing front end 312, which performsamplification, analog to digital conversion, and multiplexing functionson the signals in the sensing channels 313 a, 313 b, 313 c. Preferably,any of the electrodes 212 a-d, 214 a-d can be unused (i.e., notconnected to any sensing channel), coupled to a positive or negativeinput of a single sensing channel, coupled to the positive inputs ofmultiple sensing channels, or coupled to the negative inputs of multiplesensing channels.

A multiplexed input signal representative of all active sensing channels313 a, 313 b, 313 c is fed from the sensing front end 312 to a dataanalyzer 314. The data analyzer 314 may be a special-purpose digitalsignal processor (DSP) adapted for use in some embodiments, or in somealternative embodiments, may comprise a programmable general-purposeDSP.

In accordance with embodiments disclosed herein, the data analyzer 314includes modules configured to perform functions related to computingmeasures of phase-amplitude coupling. In an example configuration, thedata analyzer 314 includes a bandpass filter 316 that includes one ormore individual bandpass filters, a feature extraction module 318 thatincludes a peak logger 320 and a phase assignment module 322, apeak-to-phase accumulator 332, and a phase-amplitude coupling (PAC)module 336.

The bandpass filter 316 receives an amplified and digitizedelectrographic signal from the sensing front end 312 and separates thesignal into various frequency components that are of interest inmeasuring phase-amplitude coupling. For example, referring to FIG. 4A,an electrographic signal 402 may be a composite of approximately 7 Hz asa low frequency component and 50 Hz as a higher frequency component. Theelectrographic signal 402 shown in FIG. 4A is a simplified waveform usedto illustrate the function of the data analyzer 314. An actualelectrographic signal typically has more frequency and amplitudecomponents.

The electrographic signal 402 of FIG. 4A may be processed by twodifferent band pass filters within the bandpass filter 316. For example,one bandpass filer may be designed to pass the high frequency componentand the other bandpass filter may be designed to pass the low frequencycomponent. FIG. 4B illustrates the high frequency component 404 of theelectrographic signal of FIG. 4A. FIG. 4C illustrates the low frequencycomponent 406 of the electrographic signal of FIG. 4A. The frequencycomponents of the electrographic signal that are not passed by abandpass filter are attenuated.

The bandpass filters in the bandpass filter 316 are programmable so thatthe center frequencies can be selected to pass particular frequencies ofinterest. For example, bandpass filters may be programmed to pass thefollowing frequencies of interest:

-   -   Delta (1-4 Hz)    -   Theta (4-8 Hz)    -   Alpha (8-13 Hz)    -   Beta (13-25 Hz)    -   Low gamma (25-50 Hz)    -   High gamma (50-200 Hz)    -   Gamma (25 Hz high pass)    -   Pass-thru (no filtering)

In one configuration, the bandpass filters accept 10-bit digitizedelectrographic signals that are sampled at up to 1000 Hz. Thesedigitized electrographic signals are accepted from the sensing channels313 a, 313 b, 313 c of the sensing front end 312. On the output side,each bandpass filter provides an electrographic data stream, filtered inaccordance with the frequency specifications of the bandpass filter. Forexample, as shown in FIG. 3, the bandpass filter 316 may output ahigh-frequency filtered signal 324 (in the form of an electrographicdata stream) provided by a high-frequency bandpass filter. Thishigh-frequency filtered signal 324 corresponds to the high frequencycomponent of the electrographic signal, such as shown in FIG. 4B. Thebandpass filter 316 may also output a low-frequency filtered signal 326(also in the form of an electrographic data stream) provided by alow-frequency bandpass filter. This low-frequency filtered signal 326corresponds to the low frequency component of the electrographic signal,such as shown in FIG. 4C.

Each of the high-frequency filtered signal 324 and the low-frequencyfiltered signal 326 are input to the feature extraction module 318. Morespecifically, the high-frequency filtered signal 324 is input to thepeak logger 320, which is configured to identify high-frequency contentor features within the high-frequency filtered signal, and thelow-frequency filtered signal 326 is input to the phase assignmentmodule 322, which is configured to identify low-frequency content orfeatures within the low-frequency filtered signal.

In the implementation disclosed in detail herein, the feature extractionmodule 318 is configured to include one or more half wave detectors orhalf wave detection tools for purposes of detecting features thatrepresent oscillations in the electrographic signals. Half wavedetectors, however, are not the only way to detect such features.Accordingly, the feature extraction module 318 may be configured withfeature or content detectors other than half wave detectors. Forexample, filters comprised of both digital components (e.g., numericalcalculations) and analog components (e.g., resistors and capacitors) maybe used with varying degrees of success and power consumption to detectfeatures that represent oscillation in the electrographic signals.

Regarding half wave detectors, details of such detectors are describedin U.S. Pat. No. 6,810,285 titled “Seizure Sensing and Detection Usingan Implantable Device,” U.S. Patent Publication No. 2014/0276181 titled“Methods and Systems for Automatically Identifying Detection Parametersfor an Implantable Medical Device”, and U.S. Application Serial No.2017/0049351 titled “Neurological Event Detection Tools for ImplantableMedical Devices,” the disclosures of which are incorporated herein byreference.

In general, the half wave detection tool measures characteristics of anelectrographic signal related to the dominant frequency content of thesignal. The half wave detection tool processes data samplescorresponding to a portion of an electrographic signal against a set ofdetection criteria, and identifies the portion as a “qualified” halfwave when the detection criteria are satisfied. Half wave detectioncriteria may be defined by a programmed set of detection parametersincluding: a minimum amplitude threshold, a maximum amplitude threshold,a hysteresis value, a minimum duration threshold and a maximum durationthreshold.

In one implementation, a portion of an electrographic signal beingprocessed by a half wave detector is identified as a “qualified” halfwave when: 1) the difference between a local waveform minimum and alocal waveform maximum of the portion, i.e., the half wave amplitude, iswithin the minimum and maximum amplitude thresholds, with hysteresisapplied, and 2) a duration of the portion is within the minimum andmaximum duration thresholds. If the portion of the electrographic signalbeing processed satisfies only one of the amplitude criteria (withhysteresis applied) and the duration criteria, but not both, the portionof the electrographic signal is identified as an “unqualified” halfwave. For example, a half wave that satisfies the local waveform minimumand a local waveform maximum within the minimum and maximum amplitudethresholds, with hysteresis applied, but does not satisfy the minimumand maximum duration thresholds, may be identified as an “unqualified”half wave.

In the description to follow, the portion of an electrographic signalbeing process may be referred to generically as a “half wave,” withoutany “qualified” or “unqualified” designation. Only after completion ofprocessing by a half wave detector, is the portion or half waveidentified as either “qualified” or “unqualified.” Furthermore, in somemethods of computing measures of phase-amplitude coupling describedherein, unqualified half waves that do not meet the qualificationcriteria are ignored. In other methods, however, both qualified andunqualified half waves are considered when computing measures ofphase-amplitude coupling.

With reference to FIGS. 3 and 4D, the peak logger 320 is configured toprocess the high-frequency filtered signal 324 to identify and mark thetime of occurrence of high-frequency features of interest. For example,the peak logger 320 may be configured to identify the peaks 408 found inthe high frequency component 404 of the electrographic signal 402. Inone configuration, the peaks 408 are identified using a half wavemethod, and the peak logger 320 is configured to record the time ofoccurrences of peaks associated with detected qualified half waves. InFIG. 4D, these peaks 408, which may be referred to as “qualified peaks,”are defined as the highest value electrographic data sample during aqualified half wave.

With reference to FIG. 4E, the peak logger 320 may be configured,through a set of programmable detection parameters, to identify portionsof the high-frequency filtered signal 324 as qualified half waves—moreparticularly, qualified rising half waves—when the following criteriaare met:

a. The half wave 410 (e.g., the portion of the electrographic signal 324between the local minimum 414 and the local maximum 416) has an upwardslope.

b. The amplitude 412 of the half wave 410 is between a minimum amplitudethreshold value 411 and a maximum amplitude threshold value 413, whichvalues are independently selectable to favor detection of half wavesrepresenting signal content or oscillations within a high-frequencyrange. The amplitude 412 is defined as the amplitude difference betweenthe local minimum 414 and the local maximum 416 that define the halfwave 410.

c. The duration 418 of the half wave 410 is between a minimum durationthreshold value 417 and a maximum duration threshold value 419, whichvalues are independently selectable to favor detection of half wavesrepresenting signal content or oscillations within the high-frequencyrange. In one configuration, the half wave duration 418 may be definedas starting at the hysteresis crossing 426 after the local minimum 414or negative inflection and ending at the hysteresis crossing 428 afterthe local maximum 416 or positive peak. Alternatively, the half waveduration may be defined as the time elapsed between the local minimum414 and the local maximum 416 that define the half-wave.

d. Inflection regions 420, 422 of the electrographic signal 324represent a change in amplitude equal to or greater than a selectablehysteresis value 424. Inflection regions generally correspond to theportion of the electrographic signal following a change in direction ofthe signal. In FIG. 4E, inflection region 420 is defined by the localminimum 414 and the hysteresis crossing 426, while inflection region 422is defined by the local maximum 416 and the hysteresis crossing 428.

In an example configuration, a half wave detector may be tuned to detecthigh frequency waves having a frequency of around 40 Hz, based on thefollowing detection parameters:

-   -   minimum amplitude threshold=0.1 mV,    -   maximum amplitude threshold=0.2 mV,    -   hysteresis=0.05 mV,    -   minimum duration threshold of 6 msec, and    -   maximum duration threshold of 8 msec.

The peak logger 320 is configured to output time stamp information 328to the peak-to-phase accumulator 332 corresponding to the time ofoccurrence of each qualified half wave 410 in the high frequency signal.The time of occurrence of a qualified half wave may correspond to astart time or end time of the qualified half wave, or another fiducialpoint within the half wave. For example, the time of occurrence of ahalf wave may correspond to the time of the peak of the half wavecorresponding to the local maximum 416, in which case the output of thepeak logger 320 may be a time stamp that mark the time of occurrence ofthe peak 416. This peak 416 may be identified as the highest valued datasample within the set of data samples defining the qualified half wave.

In one configuration, unqualified half waves that do not meet thequalification criteria are ignored and no peaks are identified. Inanother configuration, the peak logger 320 may identify both qualifiedand unqualified half waves and output time stamp information 328 to thepeak-to-phase accumulator 332 corresponding to the time of occurrence ofeach type of half wave.

Returning to FIG. 3, the phase assignment module 322 is configured toprocess the low-frequency filtered signal 326 to identify qualified halfwaves and to assign phase ranges or phase bins to the half waves. In oneconfiguration, the phase assignment module 322 identifies qualified halfwaves in the low frequency signal and determines the start time and stoptime of phase bins associated with each qualified half wave. Both risingand falling half waves are processed and assigned phases.

With reference to FIGS. 3 and 4F, the phase assignment module 322 may beconfigured through a set of programmable detection parameters toidentify portions of a low-frequency filtered signal 326 as qualifiedhalf waves when the following criteria are met:

a. The amplitude 430 of the half wave 432 is between a minimum amplitudethreshold 433 and a maximum amplitude threshold 435, which values areindependently selectable to favor detection of half waves representingsignal content or oscillations within a low-frequency range. Theamplitude 430 is defined as the amplitude difference between the localminimum 453 and the local maximum 456 that define the half wave 432.

b. The duration 434 of the half wave 432 is between a minimum durationthreshold 437 and a maximum duration threshold 439, which values areindependently selectable to favor detection of half waves representingsignal content or oscillations within a low-frequency range.

c. Inflection regions 436, 438 of the electrographic signal 326represent a change in amplitude equal to or greater than a selectedhysteresis value 441. In FIG. 4F, inflection region 436 is defined bythe local minimum 453 and the hysteresis crossing 457, while inflectionregion 438 is defined by the local maximum 456 and the hysteresiscrossing 461

In an example configuration, a half wave detector may be tuned to detectlow frequency waves having a frequency of around 7 Hz, based on thefollowing detection parameters:

-   -   minimum amplitude threshold=1 my    -   maximum amplitude threshold=2 my    -   hysteresis=0.5 my    -   minimum duration threshold=60 msec    -   maximum duration threshold=80 msec

In one configuration, unqualified half waves that do not meet thequalification criteria are ignored and no phase bins are assigned tothem. In another configuration, the phase assignment module 322 mayidentify both qualified and unqualified half waves and assign phases toboth types of half waves.

Continuing with FIG. 4F, the number of phase ranges or phase bins 440per half wave is selectable and the phase bin numbers are assigned suchthat qualified rising half waves 432, 442 are given bin numberscontained by the range 0-8 and the qualified falling half waves 444 aregiven bin numbers contained by the range 9-17. For example, if 5 binsper half wave are selected then bin numbers 0-4 are assigned to risinghalf waves and bin numbers 9-13 are assigned to falling half waves. Allother bin numbers (5, 6, 7, 8, 14, 15, 16, and 17) are unassigned inthis example.

As shown toward the bottom of FIG. 4F, each phase bin 440 has a duration446 that is proportional to the duration 443 of the associated halfwave. Each phase bin 440 has a first time stamp that marks its start 448and a second time stamp that marks its end 450. The arrangement of thephase bins 440 depends on the selected number of bins per half wave. InFIG. 4F, examples are given for 5 phase ranges or phase bins 440 perhalf wave and for 9 phase ranges or phase bins 440 per half wave.

Each qualified half wave 432, 442, 444 is either a qualified rising halfwave 432, 442, or a qualified falling half wave 444. Each qualifiedrising half wave 432, 442 is defined as starting at a local minimum andending at a local maximum. For example, qualified rising half wave 442starts at a local minimum 452 and ends at a local maximum 454. Eachqualified falling half wave 444 is defined as starting at a localmaximum 456 and ending at a local minimum 458. The qualified half wave(QHW) line of FIG. 4F illustrates the durations of the qualified halfwaves 432, 442, 444.

Returning to the bottom of FIG. 4F, the start 448 and stop of 450 ofeach phase bin 440 is calculated based on the time of the start and endof each qualified half wave. For example, in the 5 phase-bin scenario,the duration 446 of each phase bin 440 is set to half the duration 443of the associated qualified half wave 442. In the 9 phase-bin scenario,the duration 447 of each phase bin 440 is set to one-fourth the duration443 of the associated qualified half wave 442. The start and end of eachqualified rising half wave 432, 442 is defined as the time of the localminimum 452, 453 seen prior to an up hysteresis crossing 455, 457 andthe time of the local maximum 454, 456 seen prior to a down hysteresiscrossing 459, 461. The start and end of each qualified falling half wave444 is defined as the time of the local maximum 456 seen prior to a downhysteresis crossing 461 and the time of the local minimum 458 seen priorto an up hysteresis crossing 463. Thus, for low frequency contentprocessing, the delay between the peak and the hysteresis crossing isremoved from the defined start and end of each half wave. This isdifferent from the high frequency content described with reference toFIG. 4E, where in one configuration the start and end of each half wave(the half wave duration 418) depends on hysteresis crossings 426, 428.

The data analyzer 314 calculates the duration 446, 447 as follows. Thehalf wave duration 443 is divided by 1, 2, or 4 respectively when 3, 5,or 9 phase bins per phase are selected respectively. Each phase bin 440is assigned a duration 446, 447 that is equal to the quotient, with theremainder discarded. Thus, for a 5-phase-bin implementation, theduration 446 of each phase bin is one-half of the half wave duration443. For the 9-phase-bin implementation, the duration 447 is one-fourththe half wave duration 443. In terms of data samples, if the half waveduration 443 is 54 samples and 9 bins per phase is selected, theduration 447 is the half wave duration (54) divided by 4. The quotientis 13 with a reminder of 2. Accordingly, each of the 9 phase bins may becharacterized as being 13 electrographic signal samples wide.

Each duration 446, 447 of each respective phase bin 440 is defined by astart time corresponding to the time stamp of the first data sample inthe bin, and an end time corresponding to the time stamp of the lastdata sample in the bin. In FIG. 4F, the phase bins 440 are illustratedin a stacked, overlapping manner. In terms of respective durations, thisillustrates that the start times and end times of certain phase bins maybe included in the durations of other bins; in other words, the starttimes and end times of certain phase bins may fall between the starttime and end time of other bins. For example, in the 5-phase-binscenario, the end time 467 of bin 1 is included in the duration of bin2. Likewise, the end time 465 of bin 2 is included in the duration ofbin 3. The overlapping of bins also illustrates that the phase bins 440cover the half wave duration 443 with approximately one-half bin widthoverhanging the start and end of the half wave. Thus, in the 5-phase-binscenario, a portion of the duration of bin 0 and bin 4 are outside ofthe half wave duration 443, while in the 9-phase-bin scenario, a portionof the durations of bin 0 and bin 8 are outside of the half waveduration.

Returning to FIG. 3, the phase assignment module 322 is configured tooutput time information 330 to the peak-to-phase accumulator 332corresponding to a series of time stamps that mark the start 448 and end450 of each phase bin 440. The time information 330 may be provided as aseries or index of time stamps.

The peak-to-phase accumulator 332 receives the time stamp information328 from the peak logger 320 and the time stamp information from thephase assignment module 322 as inputs and processes these time stamps todetermines—for each phase bin—how many high frequency peaks (wherein,each peak corresponds to a time stamp from the peak logger 320) happencoincident with the phase bin (wherein, each phase bin is defined by apair of consecutive time stamps from the phase assignment module 322,which time stamps represent the beginning and end of the phase bin). Asused herein, “coincident” is defined as happening after the start 448 ofa phase bin 440 but before the end 450 of the phase bin. Accordingly, atime stamp from the peak logger 320 having a value between the value ofthe start time stamp and the value of the end time stamp of a phase binis considered coincident with that bin.

The peak-to-phase accumulator 332 determines how many time stampsincluded in the time stamp information 328 from the peak logger 320occur during the times marked as the start and end of each phase bin bythe time information 330 from the phase assignment module 322. The dataoutput 334 of the peak-to-phase accumulator 332 is a counthigh-frequency (HF) peaks (each of which corresponds to a qualified halfwave in the high frequency signal) for each phase bin that takes theform shown below. Each time a high frequency peak occurs during a phasebin, the count for the bin number corresponding to the phase bin isincremented. The HF peaks correspond to first features or high-frequencyfeatures of the electrographic signal, while the rising half waves andfalling half waves correspond to second features or low-frequencyfeatures of the signal.

Count of HF Bin Number Peaks 0 1 1 2 2 4 3 2 4 1 5 5 6 3 7 2 8 1 9 25 1050 11 45 12 10 13 5 14 2 15 4 16 3 17 2

The count per bin is referred to as a phase amplitude histogram. Asdescribed further below, the data analyzer 314 is configured to obtainphase amplitude histogram data 334 on a region of interest, or window,of an electrographic signal, and evaluate the data for phase-amplitudecoupling. In this regard, the data analyzer 314 includes a windowgenerator module 348 configured to generate a pulse at a regularinterval. The time between consecutive pulses defines the duration of awindow. The interval (or window duration) is selectable to be favorableto detect changes in the phase-amplitude coupling that are of interest.The range of selectable values for the window duration may range from256 msec to 4096 msec, in 256 msec steps.

Regarding window duration, a longer window duration provides a largeramount of histogram data 334 from which more sensitive measures of PACmay be obtained. A shorter window duration provides more frequentmeasure of PAC and thus allows the data analyzer 314 to detect changesin the level of PAC more quickly. However, the shorter window durationprovides a smaller amount of histogram data, which may cause the dataanalyzer to be less sensitive to small shifts in the level of PAC.Generally, a window duration is long compared to the frequency of thelow frequency electrographic signal waveform. For example, if the windowduration is set to 2 seconds and the low frequency waveform is on orderof 10 Hz, and assuming there is one qualified rising halve wave and onequalified falling half wave per cycle, there could be approximatelytwenty qualified rising half waves and twenty qualified falling halfwaves. In this case, with respect to FIG. 4F, for the 5-phase binexample there would be 20 instances of each of phase bins 0-4 and 9-13.

During a window, histogram data 334 is produced by the above describedoperations of the peak logger 320, the phase assignment module 322, andthe peak-to-phase accumulator 332. At the end of each window, thehistogram data 334 for the window is provided to the PAC module 336,where, as described further below, the data is processed to computemeasures of phase-amplitude coupling or PAC scores. The histogram data334 is then cleared or zeroed out of the peak-to-phase accumulator 332,and new histogram data is produced for a next window.

Continuing with FIG. 3, the PAC module 336 includes one or moresubmodules configured to process the histogram data 334. The submodulesmay include, for example, a window qualification module 338, a PACmeasurement module 340, and an X of Y module 346. In general, the PACmodule 336 accepts the histogram data 334 that is presented to it at theend of each window as its input, processes the data for the just-expiredwindow, and generates a PAC output 342 based on the processing outcome.The PAC module 336 may also provide diagnostic information 343 to ahistogram logger 344. Diagnostic information 343 may include, forexample, histogram data information, such as the bin number for thephase bin with the highest (lowest) number of high frequency peaks alongwith the number of peaks logged in that bin.

In one configuration, the histogram data 334 from a just-expired windowis initially processed to determine if the histogram data meets criteriafor additional processing. If the criteria are not met, the histogramdata 334 for the just-expired window is deemed disqualified and is notprocessed, and a PAC output 342 indicative of this disqualification isgenerated by the PAC module 336. In an example implementation, thewindow qualification module 338 processes the histogram data 334 againstcriteria to determine if the just-expired window is a qualified window.Criteria may relate to the quantities of features representing signalcontent or oscillations in the low-frequency range, and featuresrepresenting signal content or oscillations in the higher-frequencyrange. For example, one criterion may require that the just-expiredwindow have at least a specified number of qualified low frequencyrising and/or falling half waves. Another criterion may require that thetotal number of high frequency peaks within the just-expired window beeither greater than a minimum number or less than a maximum number.

If the histogram data 334 from a just-expired window is determined to bequalified, the data is processed by the PAC measurement module 340 toderive a measure of PAC or a PAC score, and a PAC output 342 indicativeof the PAC score is generated by the PAC module 336.

Regarding PAC scores, different algorithms may be implemented by the PACmeasurement module 340 to such scores. Examples implementations follow:

Min/Max

In this example implementation, the PAC measurement module 340identifies the histogram bin having the highest count and the histogrambin having the lowest county, and subtracts the lowest count from thehighest count to obtain a measure of PAC (or a PAC score). Referring toFIG. 4G, for the upper histogram 456 a, the PAC measurement module 340identifies histogram bin 452 a as the highest-count bin and histogrambin 454 a as having the lowest-count bin. The PAC measurement module 340subtracts the lowest count from the highest count to obtain a PAC score458 a. For the lower histogram 456 b of FIG. 4G, the PAC measurementmodule 340 identifies the histogram bin 452 b as the highest-count binand the histogram bin 454 b as the lowest-count bin, and subtracts thecounts to obtain a PAC score 458 b. In the example histograms of FIG.4G, the upper histogram 45 a has a lower PAC score 458 a.

Aggregated Bin Values

In this example implementation, the PAC measurement module 340identifies multiple histogram bins having high counts and multiplehistogram bins having low counts, accumulates the respective high countsand respective low counts, and processes the cumulative high count andthe cumulative low count to arrive at a PAC score. For example, the PACmeasurement module 340 may process the counts by determining the ratioof the cumulative high count to the cumulative low count. This ratio isthe PAC score.

Referring to FIG. 4H, with respect to the upper histogram 460, in oneembodiment, the PAC measurement module 340 may be configured to a)identify a number N of histogram bins 462 a, 462 b with the highestnumber of counts, and calculate the total number of counts in theidentified bins, b) identify a number M of histogram bins 464 a, 464 bwith the lowest number of counts, and calculate the total number ofcounts in the identified bins, and c) determine a PAC score as the ratioof the cumulative high count to the cumulative low count. M and N may bea number selected based on the total number of bins in the histogram.

With respect to the lower histogram 466 of FIG. 4H, in anotherembodiment, the PAC measurement module 340 may be configured to: a)identify a number N of contiguous histogram bins 468 a, 468 b, 468 cwith the highest number of counts, and calculate the total number ofcounts in the identified bins, b) identify the number M of contiguoushistogram bins 470 a, 470 b, 470 c with the lowest number of counts, andcalculates the total number of counts in the identified bins, and c)determine a PAC score as the ratio of the cumulative high count to thecumulative low count. Again, M and N may be a number selected based onthe total number of bins in the histogram.

Concentration Peaks

In this example implementation, the PAC measurement module 340determines the minimum number of bins required to account for a selectedpercentage of accumulated peaks. The percentage may be a programmablevalue. The determined number of bins is the PAC score. For example,referring to the table below, if the total number of HF peaksaccumulated for a just expired window is 160, and the selectedpercentage is 50%, then the PAC measurement module 340 may determine thePAC score as follows: First the PAC measurement module 340 scans throughthe list of bin numbers and associated counts to determine an order ofbins from the highest peak count to the lowest peak count.

Count of HF Bin Number Peaks 0 1 1 2 2 4 3 2 4 36 5 5 6 3 7 2 8 1 9 2510 43 11 10 12 10 13 5 14 2 15 4 16 3 17 2

In this example, the order of bins is {10, 4, 9, 11, 12, 13, 2, 15, 6,16, 1, 3, 7, 14, 2, 0, 8}. Next, the PAC measurement module 340 adds thecounts associated with the bins, in the order determined by the order ofbins, until a target count is reached or first exceeded, where thetarget count corresponds to the selected percentage of total counts. Inthis example, the target count is 50% of 160=80. The target count isfirst exceeded after adding the counts of bins 10, 4 and 9. Morespecifically: 43 (from bin 10)+36 (from bin 4)+25 (from bin 9)=104.Accordingly, the minimum number of bins it takes to reach or exceed thetarget count is 3. Thus, in this example, the PAC measurement module 340determines the PAC score is 3.

Alternatively, the PAC measurement module 340 may determine the minimumnumber of contiguous bins required to account for a selectablepercentage of accumulated peaks. The determined number of bins is thePAC score. Referring to the above table of bin counts, and working withthe same target count of 50, the PAC measurement module 340 maydetermine the PAC score by scanning through the counts in order from1-17 to identify sets of bins associated with bins having high counts.(In this implementation, the bins are not reordered from highest tolowest.) In the above example, bin 4 and bin 10 may be identified ashaving high counts and the bins on either side of these respective binsmay be considered a set of associated bins. Next, the PAC measurementmodule 340 processes the bin counts to identify the set of associatedbins having the least number of bins, that result in an accumulatedcount that reaches or exceeds the target count. In this example, the setof contiguous bins formed by bins {9, 10, 11, 12} represents the leastnumber of bins having an accumulated count that exceeds the target countof 80. More specifically: 25 (from bin 9)+43 (from bin 10)+10 (from bin11)+10 (from bin 12)=88. The other set of contiguous bins associatedwith bin 4 requires a greater number of bins to reach or exceed thetarget count. Accordingly, the minimum number of contiguous bins ittakes to reach or exceed the target count is 4. Thus, in this example,the PAC measurement module 340 determines the PAC score is 4.

Pre-Selected Bins

In this implementation, the PAC measurement module 340 determines ameasure based on the number of counts that occur during a selected setof bins. The set of bins may be selected through device programming. Themeasure is the PAC score. For example, the measure may be the percentageof peaks during a window that occur within the selected set of bins. Thepercentage is the PAC score. Referring to the table below, if the totalnumber of HF peaks accumulated for a just expired window is 160, and theselected set of bins is {9, 10, 11, 12}, then the PAC measurement module340 may determine the PAC score as follows: First, the PAC measurementmodule 340 accumulates the counts associated with the selected bins.

Count of HF Bin Number Peaks 0 1 1 2 2 4 3 2 4 36 5 5 6 3 7 2 8 1 9 2510 43 11 10 12 10 13 5 14 2 15 4 16 3 17 2

In this case, the count is: 25 (from bin 9)+43 (from bin 10)+10 (frombin 11)+10 (from bin 12)=88. Next, the PAC measurement module 340calculates the percentage the accumulated count is of the total numberof counts. In this example, the percentage is 88/160*100=55%. Thus, thePAC measurement module 340 determines the PAC score is 55%. The PACscore may be any other measure derivable from the counts. For example,the PAC score may be the ratio 88/160=0.55.

Other algorithms or processes may be implemented to obtain a measure ofphase-amplitude coupling or a PAC score for a just-expired window. Forexample, as previously mentioned, additional processes may involveconsidering both qualified half waves and unqualified half waves whencomputing a measure of phase-amplitude coupling. These additionalprocesses are described further below with reference to FIGS. 7A-11.

After obtaining a PAC score for a just-expired window, the PACmeasurement module 340 may be further configured to evaluate the PACscore, together with other information derived from the histogram datato determine if the just-expired window should be identified or flaggedas having satisfied a PAC criterion. For example, the PAC measurementmodule 340 may determine the number of qualified half waves from thelower frequency signal based on histogram data corresponding to theoutput of the phase assignment module 322, and the number of peaks fromthe higher frequency signal based on histogram data corresponding to theoutput of the peak logger 320. If all three measurements (PAC score,number of half waves, number of peaks) meet pre-selected thresholdcriteria then the PAC output 342 is set to indicate the just-expiredwindow satisfies a PAC criterion.

The PAC measurement module 340 may be further configured to determine ifPAC criteria is met for a group of windows. For example, the PACmeasurement module 340 may include an X of Y module 346 that monitorsthe individual PAC scores for a series of windows to determine when atleast X of the most recent Y windows met their individual PAC criterion.

Returning to FIGS. 2 and 3, the histogram logger 344 accepts data fromthe PAC module 336 and stores it in a memory, which may be configured asa FIFO buffer. These data are then available for the CPU 240 to collectand analyze or store for later upload through an electrographic signaldata retrieval process. The content of the data can vary depending onthe thresholding process selected in the PAC module 336. In one example,the histogram logger 344 may: a) read two bytes of data from the PACmodule 336 one time per window, 2) store the data in a memory structurefor at least 64 windows, and 3) provide a means for the CPU 240 to readthe data from the memory structure.

Following are additional examples of methods of computing measures ofphase-amplitude coupling using an implantable medical device.

Measuring Phase-Amplitude Coupling

With reference to FIGS. 5 through 11, a method of computing measures ofphase-amplitude coupling using an implantable medical device isdescribed. For example, the method may be implemented using theneurostimulation system described above with respect to FIGS. 2-4H.While details of the method are described within the context ofmeasuring theta-gamma coupling, the method is equally applicable todifferent cross frequency couplings.

Obtaining a Brain Signal

With reference to FIG. 5, at block 502 a brain signal is sensed by theimplantable medical device. The brain signal may be sensed continuouslyusing one or more of the intracranial implanted electrodes shown inFIG. 1. For example, voltage potentials sensed across implantedelectrodes may be sampled as a function of time to obtain data samplesthat may represent a composite electrographic signal including featuresacross various frequency ranges. An example of a compositeelectrographic signal 602 is shown in FIG. 6A.

At block 504, a portion of the sensed brain signal 602 is selected bythe implantable medical device for further processing. The selectedportion is referred to herein as a “region of interest” (ROI) of theelectrographic signal. In cases where the brain signal 602 iscontinuously sensed, the implantable medical device may define aprocessing window 612 of time through which the continuously sensedbrain signal passes. In FIG. 6A, the brain signal 602 may be viewed aspassing through the processing window in the direction of the arrow 614.The implantable medical device may at any given time select the portionof the brain signal 602 that is within the processing window 612 forfurther processing. The duration of the processing window 612 may beadjusted depending on the cross-frequency coupling being calculated andon a neurological state of the patient. For example, for theta-gammacoupling, the processing window may be a 5-10 second window and foralpha-beta coupling, the processing window may be a 1-2 second window.

Processing the Brain Signal

At block 505, the selected region of interest of the electrographicsignal is filtered, for example, by applying the region of interest tothe bandpass filter 316 shown in FIG. 3. Filtering the region ofinterest provides a low-frequency component 604 of the region ofinterest and a higher-frequency component 606 of the region of interest.

At block 506, the low-frequency component 604 is applied to a firstfeature extractor of the implantable medical device. The first featureextractor may be embodied as a half wave detector and may correspond tothe phase assignment module 322 of FIG. 3. The first feature extractoris configured to process the low-frequency component 604 to detectfeatures representing oscillations within a low frequency range. Thesedetected features may be referred to herein as “first features” or“low-frequency features.”

As an example, the first feature extractor may be embodied as a halfwave detector programmed with a set of detection parameters (such asdescribed above with reference to FIG. 4F) that result in detection oftheta half waves in the frequency range of 4-8 Hz. Oscillations in thetheta range are shown in the theta waveform 604 in FIG. 6B. A smallportion of a region of interest of a theta-gamma coupled brain signal702 marked with theta half wave detections is shown in FIG. 7A. The twoadjacent vertical lines 704 represent the maximum and minimum amplitudethresholds for detecting theta half waves, the adjacent horizontal lines708 represent the maximum and minimum duration thresholds for detectingtheta half waves, and the single vertical line 706 represents ahysteresis value for detecting local maxima and minima.

At block 508 the high-frequency component 606 is applied to a secondfeature extractor of the implantable medical device. The second featureextractor may be embodied as a half wave detector and may correspond tothe peak logger 320 of FIG. 3. The second feature extractor isconfigured to process the high-frequency component 606 to detectfeatures representing oscillations in a higher frequency range. Thesedetected features may be referred to herein as “second features” or“higher-frequency features”.

As an example, the second feature extractor may be embodied as a halfwave detector programmed with a set of detection parameters (such asdescribed above with reference to FIG. 4E) that result in detection ofgamma half waves in the frequency range above 40 Hz. Oscillations in thegamma range are shown in the gamma waveform 606 in FIG. 6C. A region ofinterest of a theta-gamma coupled brain signal 710 marked with gammahalf wave detections is shown in FIG. 7B. The two adjacent verticallines 712 represent the maximum and minimum amplitude thresholds fordetecting gamma half waves, the adjacent horizontal lines 714 representthe maximum and minimum duration thresholds for detecting gamma halfwaves, and the single vertical line 716 represents a hysteresis valuefor detecting local maxima and minima

Assigning Phase Ranges to Select Oscillations in the Low Frequency Range

Returning to FIG. 5, at block 510, the implantable medical devicepartitions select oscillations in the low frequency range into a numberof assigned phase ranges. Selection of oscillations in the low frequencyrange for partitioning and phase range assignment may be based onqualification of half waves. As described above, within the context ofhalf wave detectors, an oscillation in the low frequency range, e.g., alow-frequency half wave, may be either of a qualified half wave or anunqualified half wave.

With reference to FIG. 7A, qualified and unqualified half waves detectedby a theta half wave detector, are identified by graphic symbolsincluding filled, downward arrows and unfilled inverted triangles, andare numbered from 1 to 9. Within the set of theta half waves 1 to 9,theta half waves 1, 5, 6 and 7 are qualified theta half waves becauseeach meets the set amplitude and duration criteria, i.e., theiramplitudes are within the set minimum and maximum amplitude thresholdsand their durations are within the set minimum and maximum durationthresholds. Theta half waves 2, 3, 4 are unqualified theta half wavebecause each has a duration that exceeds the set maximum durationthreshold. Theta half waves 8 and 9 are unqualified theta half wavesbecause each has an amplitude less than the set minimum amplitudethreshold.

With reference to FIG. 7B, qualified and unqualified half waves detectedby a gamma half wave detector are identified by graphic symbolsincluding filled, downward arrows and unfilled inverted triangles. Byapplying rules for hysteresis, and amplitude and duration thresholdssimilar to those described above for the theta half-wave detector, thesecond half wave detector for gamma half waves identifies qualified andunqualified gamma half waves in accordance with the legend shown in FIG.7B.

Returning to FIG. 5, in one implementation of the method of computingmeasure of phase-amplitude coupling using an implantable medical device,only qualified oscillations in the low frequency range, e.g., qualifiedlow-frequency half waves, are processed. Accordingly, at block 510, ifno qualified oscillations in the low frequency range are identified, theprocess returns to block 504, where a next portion of the continuouslymonitored brain signal is selected for processing. If qualifiedoscillations in the low frequency range are detected at block 510, theprocess proceeds with phase assignment. In an alternate implementation,both qualified and certain unqualified oscillations in the low frequencyrange may be processed. The following description of phase rangeassignment is made with respect to the implementation that processesonly qualified half waves. The phase range assignment procedure,however, is equally applicable to an implementation that used bothqualified and unqualified half waves. An implementation that uses bothqualified and unqualified half waves is described later below.

Assigning Phase Ranges

Continuing with block 510, in the case where qualified oscillations inthe low frequency range are selected for processing, each of thequalified oscillations in the low frequency range identified in block510 are further processed on an individual basis, with some commonalitydepending on whether a qualified half wave is a rising half wave or afalling half wave. A rising half wave has an upward slope; while afalling half wave has a downward slope. In general, each qualifiedoscillation in the low frequency range is divided into a number ofportions or phases that span an equal number of phase ranges. Eachportion or phase of the qualified oscillation in the low frequency rangeis assigned a phase range or phase bin. Common phase ranges are assignedto qualified oscillations in the low frequency range having the sameslope. In other words, each downward sloping portion of an oscillationin the low frequency range has the same phase ranges, and each upwardsloping portion of an oscillation in the low frequency range has thesame phase ranges.

An example of a partitioning of qualified oscillations in the lowfrequency range—in this case, qualified theta half waves—is illustratedin FIG. 8, wherein a region of interest of a theta-gamma coupled brainsignal 802 is marked with four qualified theta half wave detections 804,806, 808, 810. The first qualified theta half wave 804 corresponds tothe portion of the brain signal 802 bounded by dashed box 812. Thesecond qualified theta half wave 806 corresponds to the portion of thebrain signal 802 bounded by dashed box 814. The third qualified thetahalf wave 808 corresponds to the portion of the brain signal 802 boundedby dashed box 816. The fourth qualified theta half wave 810 correspondsto the portion of the brain signal 802 bounded by dashed box 818. Eachof the dashed boxes 812, 814, 816, 818 is partitioned in three by twovertical lines. The first qualified theta half wave 804, the secondqualified theta half wave 806, and the fourth qualified theta half wave810 are downward sloping, while the third qualified theta half wave 808is upward sloping.

Returning to the first qualified theta half wave 804, this half wave isdivided into “n” equal portions, wherein each portion is bounded by anadjacent pair of the dashed vertical lines. In this example, n=3. Aphase range is assigned to the portion of the half wave spanning betweenthe dashed vertical lines. In this case the spanned phase range is 180degrees and is bounded on the left by the half wave maximum 820 at 90degrees and on the right by the half wave minimum 824 at 270 degrees.Each pair of adjacent dashed vertical lines is allotted a portion of theoverall phase range, in this example 60 degrees. Phase range values maybe assigned to different portions of a qualified half wave depending,for example, on the half waves similarity in position to a sine wave ofsimilar frequency as shown here.

In this example, the portion of the first qualified theta half wave 804spanning between the first pair of vertical lines includes the positivepeak 820 or local maxima of the first qualified theta half wave and isassigned the phase range of 90 degrees to 150 degrees. The portion ofthe first qualified theta half wave 804 spanning between the second pairof vertical lines is assigned the phase range of 150 degrees to 210degrees. The portion of the first qualified theta half wave 804 spanningbetween the third pair of vertical lines corresponds to the phase range210 degrees to 270 degrees. The second qualified theta half wave 806 andthe fourth qualified theta half wave 810 have the same type of slope,i.e., downward slope, as the first qualified theta half wave 804 andthus are assigned the same phase ranges as the first qualified halfwave.

The third qualified theta half wave 808 is upward sloping. Like theother qualified theta half waves 804, 806, 810, the third qualifiedtheta half wave 808 is divided into three portions, wherein each portionis bounded by an adjacent pair of the dashed vertical lines. A phaserange is assigned across the span of the dashed vertical lines. Theoverall phase range is 180 degrees, just like the downward slopingqualified theta half waves 804, 806, 810, however, the overall phaserange of the third qualified theta half wave 808 is bound on the left bythe half wave minimum 822 at 270 degrees and on the right by the halfwave maximum 826 at 90 degrees, which is different from the boundariesof the downward sloping qualified half waves. Each pair of adjacentdashed vertical lines is allotted a portion of the overall phase range.In this example, the portion of the third qualified half wave 808spanning between the first pair of vertical lines includes the negativepeak 822 or local minima of the third qualified theta half wave 808 andis assigned the phase range of 270 degrees to 330 degrees. The portionof the third qualified theta half wave 808 spanning between the secondpair of vertical lines is assigned the phase range of 330 degrees to 30degrees. The portion of the third qualified theta half wave 808 spanningbetween the third pair of vertical lines is assigned the phase range 30degrees to 90 degrees.

The foregoing partitioning of qualified theta half waves 804, 806, 808,810 and assignment of phase ranges may be implemented by the implantablemedical device through the grouping of signal samples into bins and thecorrelating of bins to phase ranges. For example, with reference to FIG.9A, a first qualified theta half wave 902 having a downward slope, and asecond qualified theta half wave 904 having an upward slope arehighlighted. A detail of the first qualified theta half wave 902,together with its corresponding assignment of signal-sample-groupings tobins, and bins to phase ranges, is illustrated in FIG. 9B. Likewise, adetail of the second qualified theta half wave 904, together with itscorresponding signal sample groupings to bins, and bins to phase ranges,is illustrated in FIG. 9C.

With reference to FIGS. 9A and 9B, when processing the first qualifiedtheta half wave 902 the implantable medical device determines that thelocal maxima 906 of the half wave is at sample number “233,” and thatthe local minima 908 is at sample “250.” Based on these determinations,the device identifies that this theta half wave 902 has a downwardslope. The device determines the number of signal samples spanning thefirst qualified theta half wave 902 based on the difference in thesample number of the local minima 908 (sample “233”) and the samplenumber of the local maxima 906 (sample “250”). In this example, thereare 18 data samples spanning the first qualified theta half wave 902.

The device groups samples to corresponding phase bins by dividing thetotal number of data samples spanning the first qualified theta halfwave 902 into a number (n). The number (n) may be programmed into thedevice. In the example of FIG. 9B, n=3 and the 18 samples are thusdivided into three bins 910, 912, 914, identified as bins 1, 2, and 3,respectively. The first bin 910 contains samples between sample number233 to 238, the second bin 912 contains samples between 239 to 244, andthe third bin 914 contains samples between 245 to 250. Note that in thisexample, the number of data samples between the first qualified thetahalf wave 902 is exactly divisible by the number of bins. In cases wherethe number of samples is not exactly divisible by the number (n) ofbins, the device may choose some bins to be allotted more samples thanother bins. The bins that receive more samples may be selected atrandom.

With reference to FIGS. 9A and 9C, when processing the second qualifiedtheta half wave 904 the implantable medical device determines that thelocal maxima 916 of the half wave is at sample number “1250,” and thatthe local minima 918 is at sample “1223.” Based on these determinations,the device identifies that the second qualified theta half wave 904 hasan upward slope. The device determines the number of signal samplesbetween the second qualified theta half wave 904 based on the differencein the sample number of the local maxima 916 (sample “1250”) and thesample number of the local minima 918 (sample “1223”). In this example,there are 28 data samples between the second qualified theta half wave904.

The device groups samples to corresponding bins by dividing the totalnumber of data samples spanning the second qualified theta half wave 904into a number (n). In the example of FIG. 9C, n=3 and the 28 samples arethus divided into three bins 920, 922, 924, identified as bins 4, 5, and6 respectively. Since the slope of the second qualified theta half wave904 is different from the slope of the first qualified theta half wave902, the bin numbers (bin 4, bin 5, bin 6) assigned to the secondqualified theta half wave are different than the bin numbers (bin 1, bin2, bin 3) assigned to the first qualified theta half wave. If the slopesof the first and second qualified half waves 902, 904 are the same, thesame bin numbers are assigned to the samples.

Continuing with FIG. 9C, the fourth bin 920 contains samples betweensample number “1223” to “1231,” the fifth bin 922 contains samplesbetween “1232” to “1240,” and the sixth bin 924 contains samples between“1241” to “1250.” Note that the number of bins (n=3) assigned to each ofthe downward sloping first qualified theta half wave 902 and the upwardsloping second qualified theta half wave 904 is the same. As a result,the number of samples in each of bins 4, 5, and 6, of the secondqualified theta half wave 904 is greater than the number of samples inbins 1, 2, and 3 of the first qualified theta half wave 902. It is notedthat the total number of bins for the two half waves comprising thedownward sloping first qualified theta half wave 902 and the upwardsloping second qualified theta half wave 904 is 6.

Continuing with FIGS. 9A, 9B, and 9C, the implantable medical deviceassigns phase ranges to each of the bins 1 through 6. In a perfect sinewave, 90 degrees is the local maxima and 270 degrees is the localminima. Similar phase values may be assigned to similar portions ofqualified theta half waves 902, 904 identified by the device. Forexample, the point in each qualified theta half wave 902, 904 containingthe local maxima may be assigned phase 90 degrees and the point in eachqualified theta half wave containing the local minima will be assignedphase 270 degrees. This is evident in FIGS. 9B and 9C, wherein the bin910 (bin 1) containing the local maxima 906 (sample “233”) of the firstqualified theta half wave 902, and the bin 924 (bin 6) containing thelocal maxima 916 (sample “1250”) of the second qualified theta half wave904, are each assigned a phase range that includes phase 90 degrees.Likewise, the bin 914 (bin 3) containing the local minima 908 (sample“250”) of the first qualified theta half wave 902, and the bin 920 (bin4) containing the local minima 918 (sample “1223”) of the secondqualified theta half wave 904, are each assigned a phase range thatincludes phase 270 degrees.

The cumulative phase range across the total number of bins may be 360degrees, with the bins of the downward sloping or falling qualified halfwaves spanning a total of 180 degrees and the bins of the upward slopingor rising qualified half waves also spanning a total of 180 degrees.Within each group of bins, e.g., bins of the downward sloping half waves(bins 1 to 3) and bins of the upward sloping half waves (bins 4-6), the180 degrees may be evenly divided, with each individual bin spanning 60degrees.

Computing a Measure of Phase-Amplitude Coupling

Returning to FIG. 5, at block 512, the implantable medical devicedetermines a metric based on counts of oscillations in the higherfrequency range, e.g., higher-frequency half waves, or gamma half waves.Specifically, the device determines a metric for each portion of thelow-frequency oscillations assigned a phase range or phase bin in block510. The determined metric is based on a count of oscillations in thehigher frequency range, e.g., gamma half waves, that coincide with theportion of the oscillations in the low frequency range, e.g., theta halfwaves. “Coincide” in this context means that an oscillation in thehigher frequency range occurs in the same portion of the brain signal asan oscillation in the low frequency range.

At block 513, the implantable medical device determines an aggregatemetric for each assigned phase range or phase bin. The aggregate metricis based on the individual metrics determined for each portion of thedetected feature having the phase range or phase bin assigned thereto.

At block 514, the implantable medical device determines a measure ofphase-amplitude coupling, e.g., a phase-amplitude coupling index, basedon the determined metrics. Detailed descriptions of blocks 512, 513 and514 follow.

Higher Frequency Oscillation Metrics

In one implementation of determining metrics for use in calculating ameasure of phase-amplitude coupling, a metric, e.g., count or number, ofoscillations in the high frequency range, e.g., gamma half waves, isdetermined for each phase range or phase bin, of each oscillation in thelower frequency range, e.g., theta half wave. An aggregate metric isthen determined based on the individual metrics.

With reference to FIG. 10, which is an enlarged illustration of thetheta-gamma coupling signal of FIG. 9, four qualified oscillations inthe low frequency range, e.g., theta half waves 1002, 1004, 1006, 1008,are shown together with qualified oscillations in the higher frequencyrange, e.g., gamma half waves 1010. Each of the qualified theta halfwaves 1002, 1004, 1006, 1008 is partitioned into three portions asindicated by the vertical dashed lines, with each portion being assignedto a bin number. The bin number assignments correspond to theassignments described above with respect to FIGS. 9A, 9B, and 9C; andare indicated in FIG. 10 by numbers 1 through 6 at the top of the dashedboxes.

The implantable medical device counts the number of qualified gamma halfwaves 1010 that are detected in each bin of each qualified theta halfwave 1002, 1004, 1006, 1008 or more specifically within the data samplesin each bin of a qualified theta half wave. The bins correspond to phaseranges and qualified gamma half waves 1010 detected within the datasamples in a particular bin are considered to “coincide” with theportion of the qualified theta half wave 1002, 1004, 1006, 1008associated with the particular bin.

These counts of qualified gamma half waves 1010 correspond to individualmetrics. For example, for qualified theta half wave 1002, the devicedetects one qualified gamma half wave 1012 in bin 1, and no qualifiedgamma half waves in either of bin 2 or bin 3. For the qualified thetahalf wave 1004, the device detects one qualified gamma half wave 1014 inbin, and no qualified gamma half waves in either of bin 2 or bin 3. Forqualified theta half wave 1006, the device detects no qualified gammahalf waves in bin 4, one qualified gamma half wave 1016 in bin 5, andfour qualified gamma half waves 1018 in bin 6. For qualified theta halfwave 1008, the device detects one qualified gamma half wave 1020 in bin1, and no qualified gamma half waves in either of bin 2 or bin 3. Atable summarizing these individual metrics, e.g., counts, follows:

TABLE 1 Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6 (phase 90 (phase 150 (phase210 (phase 270 (phase 330 (phase 30 to 150) to 210) to 270 to 330) to30) to 90) # of 1 0 0 n/a n/a n/a qualified gamma HWs (for theta HW1002) # of 1 0 0 n/a n/a n/a qualified gamma HWs (for theta HW 1004) #of n/a n/a n/a 0 1 4 qualified gamma HWs (for theta HW 1006) # of 1 0 0n/a n/a n/a qualified gamma HWs (for theta HW 1008 Aggregate 3 0 0 0 1 4(sum)

In Table 1, an aggregate metric for each bin is obtained by summing theindividual metrics within a bin. Alternatively, the aggregate metric maybe a statistical measure of the individual metrics, such as an averageof the individual metrics within a bin.

The brain signal of FIG. 10 is approximately one second in duration toallow for clarity of illustration. Accordingly, the total number ofgamma half wave counts in Table 1 is small. However, as previouslydescribed, the implantable medical device may process a brain signal ofa particular duration for a particular application through the use of aprogrammable processing window 612. For example, a user may program thedevice to process a 5 second duration of the brain signal. In this case,the number of gamma half wave counts would likely be higher and therebypresent more data upon which to base a measure of phase-amplitudecoupling.

In another implementation of determining metrics to be used to calculatea measure of phase-amplitude coupling, the metric, e.g., count ornumber, of gamma half waves is normalized relative to a measure of theduration of the portion of the low-frequency oscillation or the durationof the phase bin. The measure of the duration of the low-frequencyoscillation portion or phase bin may correspond to the number of datasamples in the portion or phase bin. Accordingly, a normalized count ofgamma half waves may be obtained by dividing the number of gamma halfwaves within a bin of a theta half wave by the number of samples in thebin. The gamma half waves may be qualified only, or both qualified andunqualified.

With reference to FIGS. 9A, 9B, 9C, and 10, in an example of the processof normalizing gamma half waves, the implantable medical device countsthe number of qualified gamma half waves 1010 that are detected in eachbin of each qualified theta half wave 1002, 1004, 1006, 1008, that is,within the data samples in each bin of a qualified theta half wave. Forexample, for qualified theta half wave 1002, the device detects onequalified gamma half wave 1012 in bin 1, and no qualified gamma halfwaves in either of bin 2 or bin 3. As shown in FIG. 9B, bin 1 includes 6data samples. Accordingly, the individual metric for bin 1 of qualifiedtheta half wave 1002 is ⅙. For qualified theta half wave 1004, thedevice detects one qualified gamma half wave 1014 in bin 1, and noqualified gamma half waves in either of bin 2 or bin 3. Bin 1 includes 6data samples, so the individual metric for bin 1 of qualified theta halfwave 1004 is ⅙.

For qualified theta half wave 1006, the device detects no qualifiedgamma half waves in bin 4, one qualified gamma half wave 1016 in bin 5,and four qualified gamma half waves 1018 in bin 6. As shown in FIG. 9C,each of bin 4 and bin 5 includes 9 data samples. Accordingly, theindividual metric for bin 4 of qualified theta half wave 1002 is 1/9,and the individual metric for bin 5 is 4/9. For qualified theta halfwave 1008, the device detects one qualified gamma half wave 1020 in bin1, and no qualified gamma half waves in either of bin 2 or bin 3. Asshown in FIG. 9B, bin 1 includes 6 data samples. Accordingly, theindividual metric for bin 1 of qualified theta half wave 1008 is ⅙. Atable summarizing these counts follows:

TABLE 2 Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6 (phase 90 (phase 150 (phase210 (phase 270 (phase 330 (phase 30 to 150) to 210) to 270 to 330) to30) to 90) # of 1/6 0 0 n/a n/a n/a qualified gamma HWs/ number of datasamples (for theta HW 1002) # of 1/6 0 0 n/a n/a n/a qualified gammaHWs/ number of data samples (for theta HW 1004) # of n/a n/a n/a 0 1/94/10 qualified gamma HWs/ number of data samples (for theta HW 1006) #of 1/6 0 0 n/a n/a n/a qualified gamma HWs/ number of data samples (fortheta HW 1008) Aggregate 3/6 0 0 0 1/9 4/10 (sum)

The individual metrics of each bin are further processed by aggregatingthe metrics. For example, an aggregate metric of a bin may be obtainedby summing the individual metrics associated with the bin. This type ofaggregation is reflected in Table 2. Alternatively, the aggregate may bea statistical measure of the individual metrics associated with the bin,such as an average.

Measure of Phase-Amplitude Coupling

Returning to FIG. 5, after determining metrics for the oscillations inthe higher frequency range, e.g., gamma half waves, the process proceedsto block 514, wherein the implantable medical device determines ameasure of phase-amplitude coupling or a PAC score based on the metrics.

In one implementation described above, referred to as the “Min/Max”implementation, a PAC score is computed as the difference between themaximum aggregate metric and minimum aggregate metric across allassigned phase ranges, as follows:PAC score=max entry−min entry  (Eq. 1)

-   -   where, max entry=the maximum aggregate metric, and        -   min entry=the minimum aggregate metric

If the PAC score is 0, then gamma has no preference for the differentphases of theta, and phase-amplitude coupling is considered absent. Anon-zero PAC score indicates that measurable phase-amplitude coupling ispresent. With reference to the aggregate metrics in Table 1, the PACscore=4−0=4. A PAC score of 4 reflects measurable gamma coupling withtheta. In other words, a PAC score of 4 reflects the presence ofcoupling in time between two different frequencies. The location of thetheta-gamma coupling corresponds to the bin having the maximum aggregatemetric. In Table 1, the largest aggregate metric is 4, and is in bin 6,which corresponds to phases between 30 and 90 degrees. Accordingly, inthe example of Table 1, most oscillations in the higher frequency range,e.g., gamma oscillations, are coupled to phases between 30 and 90degrees of oscillations in the low frequency range, e.g., thetaoscillations.

With respect to the aggregate metrics in Table 2, the PACscore=3/6−0=0.5. A PAC score of 0.5, as in Table 2, reflects measurablegamma coupling with theta. In Table 2, the largest aggregate metric is3/6, and is in bin 1, which corresponds to phase 90 to 150. Accordingly,in the example of Table 2, most of the oscillations in the higherfrequency range, e.g., gamma oscillations, are coupled to phase 30 to 90of oscillations in the low frequency range, e.g., theta oscillations.

The foregoing process of calculating a measure of phase-amplitudecoupling may be performed periodically, e.g., once an hour, once aminute, etc., or continuously, on a rolling basis. In the case ofperiodic performance, the implantable medical device captures theportion of a brain signal within a processing window 612, such as shownin FIG. 6, and calculates a measure of phase-amplitude coupling based onthat portion. The duration of the processing window may range fromseveral milliseconds to several hundreds of seconds.

In the case of continuous performance, the implantable medical devicecontinuously analyzes a continuously sensed brain signal and detects forqualified oscillations in the low frequency range, and for qualifiedoscillations in the higher frequency range that coincide with qualifiedoscillations in the low frequency range. Metrics of oscillations in thehigher frequency range, as described above, are computed on a rollingbasis, using a current set of metrics. The current set of metrics may bethe most metrics obtained from a most recent “x” seconds of the brainsignal, where “x” may be programmed into the device.

Other Calculations of Phase-Amplitude Coupling

In another implementation, instead of computing a PAC score using Eq. 1,a set of rules may be applied to the counts in the bins to determine ifsignificant phase-amplitude coupling is present. For example, theimplantable medical device may be programmed to monitor the total countof second-frequency oscillations in the higher frequency range, e.g.,gamma half waves, in each of a number of bins and then process thecollection of counts to determine if the counts are skewed toward asubset of the bins. Essentially, the device processes the counts todetermine if the majority of the counts occur in a few bins, or if thereis a more even distribution of counts across all bins. For example, ifthere are 100 counts and ten bins, the device may conclude that thecounts are not skewed if there are 10 counts in each of the ten bins.However, if three of the bins each contain 30 counts, and the remaining10 counts are dispersed among the remaining seven bins, then the devicemay conclude that the counts are skewed.

In terms of a measure of phase-amplitude coupling, a case where thecounts are skewed to a small number of bins implies that there is a highlevel of phase-amplitude coupling, whereas a more even distribution ofthe counts among the bins implies a low level of phase-amplitudecoupling. In general terms, the device may process the counts todetermine if more than x % of entries in the bins are distributed amongy % of the total number of bins. The values of x and y determine whetherthe outcome indicates a high measure of phase-amplitude coupling or alow measure. For example, if x=80 and y=30, then an outcome thatindicates that 80% of the counts are distributed among 30% of the binsindicates a skewing of counts and a high measure of phase-amplitudecoupling. Whereas, if x=80 and y=80, then an outcome that indicates that80% of the counts are distributed among 80% of the bins indicates aneven distribution of counts and a low measure of phase-amplitudecoupling.

As noted above, the counts in the bins may be computed on a rollingbasis on x seconds worth of data. In this way, the counts in the binsare continuously updated with a moving processing window 612 that runsover new portions of a continuously sensed brain signal, as the brainsignal becomes available. Counts in the bins correspond to the neuralsignal in the moving processing window and as the window moves to thenext brain signal sample(s), the older counts in the bin that no longercorrespond to the brain signal within the new processing window areoverwritten by new entries. As previously described, the user may selectthe processing window to be a few milliseconds long or several secondsdepending on the application. The device may be programmed to store (atleast temporarily) all counts in the bins for subsequent downloading toan external computer for further processing and viewing by a user.

Measure of Phase-Amplitude Coupling Based on Qualified and UnqualifiedHalf Waves

As previously described, in an alternate implementation, both qualifiedand certain unqualified half waves may be used to compute a measure ofphase-amplitude coupling. If an oscillation in a low frequency range,e.g., a theta half wave, is deemed unqualified because of its amplitudenot meeting the minimum and maximum amplitude threshold criteria, theunqualified theta half wave may still be processed together withqualified oscillations in the low frequency range to calculate a measureof phase-amplitude coupling.

The unqualified nature of these theta half waves is accounted for in theprocess by assigning a weight to the bins into which the unqualifiedtheta half waves fall. A bin may be assigned a weight that depends onthe difference or the ratio between the amplitude of the unqualifiedtheta half wave and the minimum or maximum amplitude threshold the thetahalf wave failed to satisfy. For example, if the amplitude of the thetahalf wave is 2 mV and the minimum amplitude threshold is at 2.5 mV, aweight of 2/2.5, which equals 0.8, may be assigned to the binscorresponding to this particular unqualified theta half wave.

Similarly, if a theta half wave is deemed unqualified because itsduration does not satisfy the minimum and maximum duration thresholdcriteria, the unqualified theta half wave may be processed together withqualified theta half wave to calculate a measure of phase-amplitudecoupling. The unqualified nature of these theta half waves is accountedfor in the process by assigning a weight to the bins into which suchunqualified theta half waves fall. For example, a bin may be assigned aweight that depends on the difference or the ratio between the durationof the unqualified half wave and the duration threshold the theta halfwave failed to satisfy. For example, if the duration of the theta halfwave is 3 msec and the maximum duration threshold is 2.8 msec, a weightof 1−((β−2.8|)/2.8), which equals 0.93, may be assigned to the binscorresponding to this particular unqualified half wave.

For theta half waves that do not meet amplitude and durationconstraints, both amplitude and duration threshold based weights may becombined to derive a new weight in such cases. The product of therespective weights may be served as a combined weight. For example, ifthe amplitude threshold weight is 0.8 and the duration threshold weightis 0.93, the combined weight would be 0.8*0.93=0.74.

With reference to FIG. 11, which is an enlarged illustration of thetheta-gamma coupled signal of FIG. 8, four qualified oscillations in thelow frequency range, e.g., theta half waves 1102, 1104, 1106, 1108, andtwo selected unqualified oscillations in the low frequency range, e.g.,unqualified theta half waves 1124, 1126, are shown together withqualified oscillations in the higher frequency range, e.g., gamma halfwaves 1110 and unqualified oscillations in the higher frequency range,e.g., gamma half waves 1128. In this example, the unqualified theta halfwaves 1124, 1126 are selected for analysis along with the qualifiedtheta half waves 1102, 1104, 1106, 1108 because of their failure tosatisfy the amplitude criteria, while the other three unqualified thetahalf waves 1130, 1132, 1134 are excluded from the analysis for notmeeting the duration criteria. In other instances, half waves that areunqualified based on duration may be selected for inclusion in theanalysis over those that are unqualified based on amplitude. In othercases, all unqualified half waves may be selected for analysis.

Continuing with FIG. 11, each of the qualified theta half waves 1102,1104, 1106, 1108 is partitioned into three portions as indicated by thevertical dashed lines, with each portion being assigned to a bin number.Each of the unqualified theta half waves 1124, 1126 is also partitionedinto three portions as indicated by the vertical dashed lines, with eachportion being assigned to a bin number. The bin number assignmentcorresponds to the assignments described above with respect to FIGS. 9Band 9C, and are indicated in FIG. 11 by numbers 1 through 6 at the topof the dashed boxes.

The implantable medical device counts the number of qualified gamma halfwaves 1110 that are detected in each bin of each qualified theta halfwave 1102, 1104, 1106, 1108 or more specifically within the data samplesin each bin of a qualified theta half wave. The bins correspond to phaseranges and qualified gamma half waves 1110 detected within the datasamples in a particular bin are considered to “coincide” with theportion of the theta half wave associated with the particular bin.

These counts of qualified gamma half waves 1110 correspond to individualmetrics. For example, for qualified theta half wave 1102, the devicedetects one qualified gamma half wave 1112 in bin 1, and no qualifiedgamma half waves in either of bin 2 or bin 3. For qualified theta halfwave 1104, the device detects one qualified gamma half wave 1114 in bin1, and no qualified gamma half waves in either of bin 2 or bin 3. Forqualified theta half wave 1106, the device detects no qualified gammahalf waves in bin 4, one qualified gamma half wave 1116 in bin 5, andfour qualified gamma half waves 1118 in bin 6. For qualified theta halfwave 1108, the device detects one qualified gamma half wave 1120 in bin1, and no qualified gamma half waves in either of bin 2 or bin 3. Thesefour theta half waves 1102, 1104, 1106, 1108 are qualified. Thus, theweights associated with each of these theta half waves is 1.

The implantable medical device also counts the number of qualified gammahalf waves 1110 that are detected in each bin of each unqualified thetahalf wave 1124, 1126. For example, for unqualified theta half wave 1124,the device detects 3 qualified gamma half waves 1119 in bin 4 and noqualified gamma half waves in either of bin 5 or bin 6. For unqualifiedtheta half wave 1126, the device detects one qualified gamma half wave1120 in bin 1, and no qualified gamma half waves in either of bin 2 orbin 3. These two theta half waves 1124 and 1126 are unqualified. Thus, alower weight is associated with each of these.

As described above, the weight for an unqualified theta half wave may bebased on the difference or the ratio between the amplitude of theunqualified theta half wave and the minimum or maximum amplitudethreshold the theta half wave failed to satisfy. The weight may also bebased on the difference or the ratio between the duration of theunqualified half wave and the duration threshold the theta half wavefailed to satisfy. In this example, theta half wave is 1124 assigned aweight of 0.7, and the theta half wave 1126 is assigned a weight of 0.4.A table summarizing these individual metrics, e.g., counts, and weightsfollows:

TABLE 3 Weight Bin 2 Bin 3 Bin 4 Bin 5 based Bin 1 (phase (phase (phase(phase Bin 6 on (phase 90 150 to 210 to 270 to 330 to (phase theta to150) 210) 270 330) 30) 30 to 90) HW # of 1 0 0 n/a n/a n/a 1 qualifiedgamma HWs (theta HW 1102) # of 1 0 0 n/a n/a n/a 1 qualified gamma HWs(theta HW 1104) # of n/a n/a n/a 0 1 4 1 qualified gamma HWs (theta HW1106) # of 1 0 0 n/a n/a n/a 1 qualified gamma HWs (theta HW 1108) # ofn/a n/a n/a 3 0 0 0.7 unqualified (weighted gamma to 2.1) HWs (theta HW1124) # of 1 0 0 n/a n/a n/a 0.4 unqualified (weighted gamma to 0.4) HWs(theta HW 1126) Aggregate   0.85 0 0   1.05 1 2 (average)

The entries across the rows of Table 3 represent the counts of qualifiedgamma half waves detected in the corresponding bins. For those countsassociated with an unqualified theta half wave, the weight for theunqualified theta half wave is applied to the count. For example,because theta half wave 1124 is unqualified, the count in bin 4 would beweighted to 2.1 (3×0.7=2.1). Likewise, because theta half wave 1126 isunqualified, the count in bin 1 would be weighted to 0.4 (1×0.4=0.4). Inthe example of Table 3, the aggregate value for a bin is calculated bycalculating an average of the counts in the bin. For bin 1, theaggregate is (1+1+1+0.4)/4=0.85. For bin 4, the aggregate is(0+2.1)/2=1.05. For bin 6, the aggregate is (4+0)/2=2. The PAC score maybe calculated using Eq. 1 above, which results in PAC score=2−0=2.

In another implementation, unqualified oscillations in the higherfrequency range, e.g., gamma half waves, may also be processed. Similarweighing functions as described with respect to unqualified theta halfwaves may be applied for oscillations in the higher frequency range,e.g., gamma half waves, that are unqualified. However, in the case of anunqualified gamma half wave a weight is applied to the individual countof the gamma half wave. For example, if there are two qualified gammahalf waves and one unqualified gamma half wave in bin 2, and theunqualified gamma half wave has an amplitude threshold weight of 0.8,the individual counts of the two qualified gamma half waves would beunweighted and thus equal to 1, and the individual count for the oneunqualified gamma half wave would be weighted and thus equal to(1*0.8)=0.8. Thus, the aggregate metric for bin 2 would be: 1+1+0.8=2.8.If the theta half wave in which the unqualified gamma half wavecoincides happens to be an unqualified theta half wave, the individualcount of the unqualified gamma half wave may be further weighted by theweight of the unqualified theta half wave. Continuing with the precedingexample, if the unqualified gamma half wave weighted to 0.8 coincideswith an unqualified theta half wave weighted to 0.4, then the aggregatemetric for bin 2 would be: 1+1+(0.8*0.4)=2.32.

Applications of Phase-Amplitude Coupling

The implantable medical device may be configured to deliverneuromodulation therapy to the patient based on measures ofphase-amplitude coupling or PAC scores. To this end, and with referenceto FIGS. 2 and 12, the neurostimulator 110 may include a therapysubsystem 228 configured to delivery neuromodulation therapy in one ormore forms, including for example electrical stimulation and drugdelivery.

The therapy subsystem 228 includes a control interface 1210, whichreceives commands, data, and other information from the CPU 240, thememory subsystem 238, and the detection subsystem 226. The controlinterface 1210 uses the received commands, data, and other informationto control an electrical stimulator 1212, which in turn, controls astimulation signal generator 1214. The stimulation signal generator 1214is configured to generate electrical pulses and is capable of beingcoupled to one or more electrodes 212 a-212 d, 214 a-214 d through theelectrode interface 220. The stimulation signal generator 1214 receivescommands and data from the electrical stimulator 1212 and generateselectrical stimulation signals having the desired characteristics thatare properly time-correlated and applied to neurological tissue throughassociated electrodes.

The electrical stimulator 1212 is adapted to control the stimulationsignal generator to provide electrical stimulation signals appropriatefor application to neurological tissue. This can be accomplished indifferent manners. For example, in applications of therapy in responseto measures of phase-amplitude coupling to be described below, it may beadvantageous in some circumstances to provide programmed stimulation inthe form of a substantially continuous stream of pulses, or on ascheduled basis. This form of stimulation, sometimes referred to as“programmed stimulation,” is provided by a programmed stimulationfunction 1218 of the electrical stimulator 1212. Therapeutic stimulationmay also be provided in response to abnormal events or measures ofphase-amplitude coupling detected by the data analysis functions of thedetection subsystem 226. This form of stimulation, namely “responsivestimulation,” is provided by a responsive stimulation function 1220 ofthe electrical stimulator 1212. The neurostimulation therapy may beapplied in an effort to terminate a present or predicted undesiredneurological event, such an epileptic seizure or tremor due toParkinson's, or to improve cognitive ability of a patient, such asmemory improvement.

The control interface 1210 may also use the received commands, data, andother information to control a drug dispenser controller 1216, which isadapted to selectively allow the release of a drug or other therapeuticagent from a drug dispenser to one or more desired sites, within or nearthe patient's brain or elsewhere in the body. As is the case withtherapeutic electrical stimulation, drug therapy can be performed inresponse to a detected neurological event or condition, on asubstantially continuous basis or scheduled. An example drug dispenserconfigurable for use with the drug dispenser controller 1216 isdescribed in U.S. Pat. No. 8,190,270 titled “Refillable Reservoir LeadSystems,” the disclosure of which is incorporated herein by reference.

The control interface 1210 may continuously or periodically receivemeasures of phase-amplitude coupling or PAC scores from the dataanalyzer 314. The control interface 1210 is configured to process thesemeasures or scores based on intended applications of such measures. Forexample, the control interface 1210 may act on each received PAC scoreindividually by comparing a just-received, or current PAC score againsta programmed threshold and initiating neuromodulation therapy based onthe comparison outcome.

In other implementations, the control interface 1210 may be configuredto act on plurality of received PAC scores by determining a PAC indexbased on one or more individual PAC scores, and applying the PAC indexto a rule to determine whether therapy should be delivered. The PACindex may be a PAC score, or it may be a statistical measure, e.g.,mean, median, max or minimum, based on the most recent “x” number of PACscores, or may be a trend, e.g., increasing or decreasing, in a seriesof individual PAC scores or in a series of individual PAC indices, whereeach index itself is based on statistical measures of PAC scores. Therule may be based on a comparison outcome between a PAC index and a PACindex criterion. If a PAC index computed by the control interface 1210satisfies the PAC index criterion, the device may deliver therapy. Forexample, if the comparison outcome indicates that the PAC index exceedsa threshold corresponding to the PAC index criterion, the device maydeliver therapy. In a different application, stimulation may bedelivered when the comparison outcome indicates that the PAC index fallsbelow the threshold. In yet another application, an implantable medicaldevice may adjust its stimulation parameters based on the PAC indexcomparison outcome. The PAC index criterion or threshold may correspondto a value that indicates the presence of a meaningful coupling betweentheta and gamma frequencies (i.e., significant PAC) that warrantsneuromodulation therapy, in an effort to either increase the measure ofphase-amplitude coupling or decrease the measure of phase-amplitudecoupling.

Examples of areas or fields of use for the application neuromodulationtherapy based on measures of phase-amplitude coupling are disclosedbelow.

Epilepsy

An example application of neuromodulation therapy based on measures ofphase-amplitude coupling relates to epilepsy. Regarding thisapplication, it is known that phase-amplitude coupling increases atseizure onsets. C. Avarado-Rojas, M. Valderrama, A. Fouad-Ahmed, H.Feldwisch-Drentrup, M. Ihle, C. A. Teixeira, F. Sales, A.Schulze-Bonhage, C. Adam, A. Dourado, S. Charpier, V. Navarro, and QuyenM. Le Van. Slow modulations of high-frequency activity (40-140-Hz)discriminate preictal changes in human focal epilepsy. Sci Rep 4:4545,2014.

Accordingly, measures of phase-amplitude coupling based on brain signalsthat are monitored and processed in real time by the implantable medicaldevice using an embodiment comprising a technique disclosed herein, mayserve as a biomarker of seizure onset. Such a biomarker may be usefulfor diagnostic purposes (e.g., determining the nature of the onset of aseizure for the patient) as well as for treatment purposes (e.g., usingthe occurrence of a seizure onset to trigger a therapy to modulateneural activity in the hopes of stopping the seizure or at leastreducing its severity).

In one implementation, a measure of PAC computed from brain signalssensed from a patient just prior to a seizure may serve as a PAC indexcriterion against which subsequent PAC indices derived measures ofphase-amplitude coupling obtained in real time may be monitored. Asubsequent PAC index that falls within a specified range of the PACindex criterion, or a series of PAC scores that are trending toward thePAC index criterion may serve as an indication of a seizure onset.Similarly, a measure of phase-amplitude coupling computed from brainsignals sensed from a patient while the patient is at a baseline stateof neural activity (e.g., not in a seizure onset state or seizure state)may serve as a PAC index criterion against which subsequent PAC indicesmay be monitored. A subsequent PAC index that falls outside anacceptable range of the PAC index criterion (e.g., measures that are toohigh or too low), or a series of PAC scores that are trending away fromthe PAC index criterion may serve as an indication of an impendingseizure.

An indication of a seizure onset or an impending seizure may trigger theimplantable medical device (or a related implanted device) to deliver aform of electrical stimulation to the neural tissue, with an objectiveof reducing the effect of the seizure that develops after the onset andstopping or reducing the likelihood or severity of the seizure. Thedevice may also send an alert signal to the patient, so the patient maymove herself to a safe location in anticipation of a seizure.

Memory

Another example application of neuromodulation therapy based on measuresof phase-amplitude coupling relates to memory improvement. Regardingthis application, the strength of phase-amplitude coupling, particularlytheta-gamma coupling, has been shown to be directly correlated with anincrease in performance accuracy in memory tasks. A. B. Tort, R. W.Komorowski, J. R. Manns, N. J. Kopell, and H. Eichenbaum. Theta-gammacoupling increases during the learning of item-context associations.Proc Natl. Acad Sci U.S.A. 106 (49):20942-20947, 2009.

Accordingly, measures of phase-amplitude coupling (e.g., theta-gammacoupling) obtained in real time by an implantable medical device using atechnique disclosed herein, may provide a useful measure that serves asan indicator of memory performance under certain circumstances. Thedevice may deliver a neuromodulation therapy (e.g., electricalstimulation delivered according to a particular regimen or protocol)designed to keep the measure of the theta-gamma coupling at or near avalue correlated to maintaining or improving a patient's memory quality.This application may be beneficial to patients suffering from a range ofmemory related disorders such as in Alzheimer's, traumatic brain injury,or in epilepsy where memory deficits are common.

In one implementation, a PAC index criterion corresponding to a measureof PAC correlated to maintaining or improving a patient's memory qualityis obtained. The PAC index criterion may be computed from brain signalssensed from a patient while the patient is not experiencing memory loss.The PAC index criterion may also be computed from corresponding measuresobtained across a patient population that exhibit above average memorycapabilities. Subsequent PAC indices obtained in real time may bemonitored relative to the PAC index criterion. For example, a PAC indexthat falls below the PAC index criterion by a specified amount may serveas an indication of poor memory quality for the patient.

An indication of poor memory quality may trigger the implantable medicaldevice (or a related implanted device) to deliver a form of electricalstimulation to the neural tissue, with an objective of maintaining orimproving a patient's memory quality, and helping the patient withinformation processing.

Abnormal Neural Coupling

Another group of applications relate to neuromodulation strategies totreat abnormal neural coupling. Regarding this application, studies haveshown that electrical stimulation can be used to evoke cross-frequencycoupled neuronal oscillations. P. R. Shirvalkar, P. R. Rapp, and M. L.Shapiro. Bidirectional changes to hippocampal theta-gamma comodulationpredict memory for recent spatial episodes. Proc Natl. Acad Sci U.S.A.107 (15):7054-7059, 2010. Studies have also shown that phase-amplitudecoupling is vital for certain brain functions such as cognitiveprocessing. For example, it has been proposed that theta-gamma couplingof neural signals is a neural mechanism by which information processingis coordinated across multiple spatiotemporal scales in the brain. R. T.Canolty and R. T. Knight. The functional role of cross-frequencycoupling. Trends Cogn Sci 14 (11):506-515, 2010.

Thus, measures of phase-amplitude coupling (e.g., theta-gamma coupling)obtained in real time by an implantable medical device using a techniquedisclosed herein, may provide a useful measure that serves as anindicator of the quality of cognitive processing. PAC indices thatdeviate from a PAC index criterion or that fall outside a pre-determinedrange of the PAC index criterion, may serve as an indication of impairedcognitive processing. The device may deliver a neuromodulation therapy(e.g., electrical stimulation delivered according to a particularregimen or protocol) designed to keep the measure of the theta-gammacoupling at or near a value correlated to maintaining or improving apatient's cognitive processing.

In these applications, an implantable medical device may be configuredto obtain PAC indices based on measures of phase-amplitude couplingwithin a single brain area or between two brain areas that exhibitabnormal coupling. Measure of phase-amplitude coupling within a singlebrain area are computed based on brain signals sensed by a sensor (e.g.,pair of electrodes) place in or on the single brain area. Measure ofphase-amplitude coupling between two brain areas are computed based onbrain signals sensed by a sensor that spans the two areas (e.g., a firstelectrode in or on a first brain area and a second electrode in or on asecond brain area). Electrical stimulation may be delivered by thedevice in accordance with one or more of the stimulation strategiesdescribed below, to increase or decrease the measure of phase-amplitudecoupling within a single brain area or between two brain areas.

FIG. 13A-13G are illustrations of various waveforms representingdifferent electrical stimulation strategies for effecting changes inmeasures of phase-amplitude coupling. For ease of illustration, thestimulation strategies represented in FIGS. 13A-13G show electricalstimulation in the form of unipolar square wave pulses. Other, morecomplex, pulse waveforms may be used, including for example,charge-balanced biphasic waveforms with rectangular, exponential,triangular, Gaussian, and sinusoidal stimulus pulse shapes.

With reference to FIG. 13A, in one stimulation strategy, to increase themeasure of phase-amplitude coupling between two brain areas, the devicemay be configured to deliver electrical pulses 1302 to a first area ofthe brain at a first frequency, and to deliver electrical pulses 1304 toa second area of the brain at a second frequency, greater than the firstfrequency. For theta-gamma coupling, the lower, first frequency may beat or near the frequency of a filtered theta brain signal 1303 (e.g.,within the range of 4-8 Hz) and the higher, second frequency may be ator near the frequency of a filtered gamma brain signal 1305 (e.g. withinthe range of 25-200 Hz). Delivery of the electrical pulses 1302, 1304 issynchronized or near-synchronized with a fiducial point, such as themaximum amplitudes or any other desired phase, of the respective brainsignal 1303, 1305.

In an alternative stimulation strategy shown in FIG. 13B, to increasethe measure of phase-amplitude coupling at either of, or between twobrain areas, the device is configured to deliver electrical pulses 1306at a second, higher frequency in short bursts 1308 which are deliveredat a frequency equal to a first, lower frequency. For theta-gammacoupling, the lower burst frequency may be at or near the frequency of afiltered theta brain signal 1307 and the higher, second frequency of thepulses 1306 within each burst 1308 may be at or near the frequency of afiltered gamma brain signal 1309. The start of the pulse bursts 1308 issynchronized or near-synchronized with the maximum amplitudes or otherfiducial point of the filtered theta brain signal 1307. Alternatively,delivery of the bursts 1308 may be timed so that the center of the burstis synchronized or near-synchronized with the maximum amplitudes orother fiducial points of the filtered theta brain signal 1307. In eithercase, this intermittent burst stimulation may be delivered to one ormore brain areas.

Referring to FIG. 13C, in another stimulation strategy, to decrease themeasure of phase-amplitude coupling at either of, or between two brainareas, the device may be configured to deliver electrical pulses 1312 toone or more brain areas at a first, lower frequency at a specific phase,e.g., out of phase, with a filtered low frequency brain signal. Fortheta-gamma coupling, the lower frequency may be at or near thefrequency of a filtered theta brain signal 1313. Each electrical pulse1312 is delivered at a time offset from the time of a maximum amplitudeor other fiducial point of the filtered theta brain signal in an effortto interfere with the theta brain signal. In this case, while deliveryof the electrical pulse waveform 1315 is out of phase with the thetabrain signal 1313, the pulse waveform and brain signal 1313 are phaselocked in that each pulse 1312 in the pulse waveform occurs at the samephase of the brain signal.

In an alternative stimulation strategy shown in FIG. 13D, to decreasethe measure of phase-amplitude coupling between two brain areas, thedevice may be configured to deliver electrical pulses 1318 to a firstarea of the brain at a first, lower frequency, and to deliver electricalpulses 1320 to a second area of the brain at a second, higher frequencyin short bursts 1322 at a specific phase, e.g., out of phase, with afiltered lower frequency brain signal 1319. For theta-gamma coupling,the lower frequency at which electrical pulses 1318 are delivered andthe lower burst frequency at which pulse bursts 1322 are delivered maybe at or near the frequency of a filtered theta brain signal 1319. Thehigher frequency of the pulses 1320 within each burst 1322 may be at ornear the frequency of a filtered gamma brain signal 1321. Delivery ofthe electrical pulses 1318 is synchronized or near-synchronized with adesired phase, e.g., the maximum amplitude or other fiducial point, ofthe filtered theta brain signal 1319, while the start of the pulsebursts 1322 is offset from the maximum amplitudes or other fiducialpoints of the filtered theta brain signal. In this case, while deliveryof the electrical pulse-burst waveform 1323 to the second brain area isout of phase with the theta brain signal 1319 (and out of phase with thepulse waveform 1325 delivered to the first brain area), the pulse-burstwaveform 1323 and the theta brain signal are phase locked in that eachburst 1322 in electrical pulse-burst waveform occurs at the same phaseof the theta brain signal.

In yet another alternative stimulation strategy shown in FIG. 13E, todecrease the measure of phase-amplitude coupling at either of, orbetween two brain areas, the device may be configured to deliverelectrical pulses 1324 to one or more selected brain areas at afrequency different from the frequencies of observed brain signals. Inthe case of theta-gamma coupling, the pulse frequency of the electricalpulses 1324 would be different from the frequency of a filtered thetabrain wave 1327 and the frequency of a filtered gamma brain signal 1329.For example, the pulse frequency may be in a range between the 4-8 Hztheta range and the 25-200 Hz gamma range. In a variation of thisstimulation strategy, the pulse frequency may change over time. Forexample, the pulse frequency may sweep through a number of differentfrequencies between the 4-8 Hz theta range and the 25-200 Hz gammarange.

In yet another alternative stimulation strategy also shown in FIG. 13E,to decrease the measure of phase-amplitude coupling at either of, orbetween two brain areas, the device may be configured to deliverelectrical pulses 1326 to one or more selected brain areas in shortbursts 1328, wherein the pulse frequency of the electrical pulses 1326within a burst 1328, and the burst frequency at which pulse bursts 1328are delivered, are each at a frequency different from the frequencies ofobserved brain signals. In the case of theta-gamma coupling, the pulsefrequency of the electrical pulses 1326 within a burst 1328 and theburst frequency of the bursts 1328 would be different from the frequencyof a filtered theta brain wave 1327 and the frequency of a filteredgamma brain signal 1329. In a variation of this stimulation strategy,one or more of the pulse frequency and the burst frequency may changeover time. For example, each of the pulse frequency and the burstfrequency may sweep through a number of different frequencies betweenthe 4-8 Hz theta range and the 25-200 Hz gamma range.

In another alternative stimulation strategy shown in FIG. 13F, todecrease the measure of phase-amplitude coupling at either of, orbetween two brain areas, the device may be configured to deliverelectrical pulses 1330 to one selected brain area at a first pulsefrequency that is different from the frequencies of observed brainsignals, while simultaneously delivering electrical pulses 1332 toanother selected brain area at a second pulse frequency that isdifferent from the first frequency and also different from thefrequencies of observed brain signals. In the case of theta-gammacoupling, the pulse frequency of the electrical pulses 1330 and thepulse frequency of the electrical pulses 1332 would each be differentfrom the frequency of a filtered theta brain wave 1327 and the frequencyof a filtered gamma brain signal 1329. For example, the first pulsefrequency and the second pulse frequency may each be in a range betweenthe 4-8 Hz theta range and the 25-200 Hz gamma range. In a variation ofthis stimulation strategy (not shown in FIG. 13F), the device may beconfigured to simultaneously deliver electrical pulses 1330 andelectrical pulses 1332 to the same selected brain area(s). In anothervariation of this stimulation strategy, one or more of the first pulsefrequency and the second pulse frequency may change over time. Forexample, each of the first pulse frequency and the second pulsefrequency may sweep through a number of different frequencies betweenthe 4-8 Hz theta range and the 25-200 Hz gamma range.

In another alternative stimulation strategy shown in FIG. 13G, todecrease the measure of phase-amplitude coupling at either of, orbetween two brain areas, the device may be configured to deliverelectrical pulses 1334 to a selected brain area in short bursts 1336,wherein the pulse frequency of the electrical pulses 1334 within a burst1336, and the burst frequency at which pulse bursts 1336 are delivered,are each at a frequency different from the frequencies of observed brainsignals, while simultaneously delivering electrical pulses 1338 toanother selected brain areas in short bursts 1340, wherein the pulsefrequency of the electrical pulses 1338 within a burst 1340, and theburst frequency at which pulse bursts 1340 are delivered, are also eachat a frequency different from the frequencies of observed brain signals.In the case of theta-gamma coupling, the pulse frequency of theelectrical pulses 1334, the burst frequency of pulse bursts 1336, thepulse frequency of the electrical pulses 1332, and the burst frequencyof pulse bursts 1340 would each be different from the frequency of afiltered theta brain wave 1327 and the frequency of a filtered gammabrain signal 1329. For example, each of the foregoing pulse frequenciesand burst frequencies may be in a range between the 4-8 Hz theta rangeand the 25-200 Hz gamma range. In a variation of this stimulationstrategy (not shown in FIG. 13G), the device may be configured tosimultaneously deliver bursts 1336 of electrical pulses 1334 and bursts1340 of electrical pulses 1338 to the same selected brain area(s). Inanother variation of this stimulation strategy, one or more of theforegoing pulse frequencies and burst frequencies may change over time.For example, each of the pulse frequencies and the burst frequencies maysweep through a number of different frequencies between the 4-8 Hz thetarange and the 25-200 Hz gamma range.

One example of abnormal neural coupling may be abnormally decreasedphase-amplitude coupling due to injury that may be due to stroke ortraumatic brain injury. In this example, an implantable medical devicemay be configured to deliver electrical stimulation to increase themeasure of phase-amplitude coupling between the injured area of thebrain and other brain areas. For example, stimulation such as shown inFIG. 13A may be delivered to increase coupling between a region of motorcortex and sensory cortex, or between two regions of motor cortex.Alternatively, intermittent bursting stimulation as shown in FIG. 13Bmay be delivered to a single injured brain area to increase the measureof phase-amplitude coupling within that area. Measures ofphase-amplitude coupling may then be used to assess changes in couplingand subsequently select or refine first and second PAC frequencies.

Another example of abnormal neural coupling may be abnormally increasedphase-amplitude coupling in one or more brain areas due to a movementdisorder such as Parkinson's disease, essential tremor, or dystonia,resulting in tremor, rigidity or freezing. In this example, animplantable medical device may be configured to measure phase-amplitudecoupling between two or more brain areas that exhibit abnormal coupling,and electrical stimulation such as shown in FIG. 13D may be deliveredthat may decrease the observed phase-amplitude coupling between theaffected brain areas.

Another example of abnormal neural coupling may be abnormalphase-amplitude coupling in one or more brain areas associated with anyof the autistic spectrum disorders. In this example, an implantablemedical device may be configured to decrease abnormally highphase-amplitude coupling, or increase abnormally low phase-amplitudecoupling in one or more brain areas using one of the previouslydescribed stimulation strategies.

Other neurological disorders including but not limited to schizophrenia,depression, and mood disorders may be characterized by abnormal couplingwithin and between different brain areas. In these disorders, animplantable medical device may be configured to increase or decreasephase-amplitude coupling using one or more of the previously describedstimulation strategies. These treatments may help normalize neuralcoupling and lead to clinical improvements.

FIG. 14 is a flowchart of a method of delivering neuromodulation therapybased on measures of phase-amplitude coupling. The method may beimplemented by the implantable neurostimulator and leads of FIG. 2.

In block 1402, the neurostimulator derives a PAC index based onelectrical activity of a brain sensed with at least one sensorassociated with a lead. The PAC index may correspond to a PAC score ormay be derived from a plurality of PAC scores obtained over a period oftime. The PAC index may be obtained from measures of phase-amplitudecoupling computed in accordance with the methods described above withreference to FIGS. 3-11.

At block 1404, the neurostimulator evaluates the PAC index relative to aPAC index criterion to determine if the patient is, or may soon be, in astate of abnormal neural activity (e.g., seizure onset, poor memoryquality, impaired cognitive processing, abnormal neural coupling). Insome configurations, the PAC index criterion may be based on one or moremeasures of phase-amplitude coupling or PAC scores computed from brainsignals sensed while a patient was experiencing abnormal neural activity(e.g., a seizure onset, memory loss, abnormal neural coupling). In thiscase, a PAC index that falls within a specified range, e.g., 5-10%, ofthe PAC index criterion or a series of PAC scores that are trendingtoward the PAC index criterion, may be a biomarker of abnormal neuralactivity. In other configurations, the PAC index criterion may be basedon one or more measures of phase-amplitude coupling computed from brainsignals sensed while patient was in a state of normal neural activity.In this case, a PAC index that falls outside a specified acceptablerange, e.g., 5-10%, of the PAC index criterion or a series of PAC scoresthat are trending away from the PAC index criterion, may be a biomarkerof abnormal neural activity.

At block 1406, if the neurostimulator determines that the patient is, ormay soon be, in a state of abnormal neural activity, the neurostimulatordelivers a neuromodulation therapy to the patient. Neuromodulationtherapy in the form of electrical stimulation may be delivered to one ormore areas of the brain in accordance with one or more of thestimulation strategies described above to bring about a desired changein subsequently derived PAC indices by either increasing phase-amplitudecoupling or decreasing phase-amplitude coupling within an area of thebrain or between two different areas of the brain.

After delivery of neuromodulation therapy, the process returns to block1402, and is repeated. The process also returns to block 1402 if the ifthe neurostimulator determines that the patient is in a state of normalneural activity.

It should be observed that while the foregoing detailed description ofvarious embodiments of the present invention is set forth in somedetail, the invention is not limited to those details and an implantablemedical device made according to the invention can differ from thedisclosed embodiments in numerous ways. In particular, it will beappreciated that embodiments of the present invention may be employed inmany different applications to measure phase-amplitude coupling in atleast one portion of a patient's brain. It will be appreciated that thefunctions disclosed herein as being performed by hardware and software,respectively, may be performed differently in an alternative embodiment.It should be further noted that functional distinctions are made abovefor purposes of explanation and clarity; structural distinctions in asystem or method according to the invention may not be drawn along thesame boundaries. Hence, the appropriate scope hereof is deemed to be inaccordance with the claims as set forth below.

What is claimed is:
 1. An implantable medical device comprising: at least one sensor configured to be implanted in or on a brain, and to sense electrical activity of the brain; a data analyzer coupled to the at least one sensor and configured to: monitor an electrographic signal corresponding to the electrical activity of the brain sensed by the at least one sensor; for a selected portion of the electrographic signal, detect features of the electrographic signal that represent oscillations in a low frequency range, and features of the electrographic signal that represent oscillations in a higher frequency range; and determine a measure of phase-amplitude coupling between oscillations in the low frequency range and oscillations in the higher frequency range based on occurrences of oscillations in the higher frequency range which coincide with one or more phases of oscillations in the low frequency range; and a therapy subsystem coupled to the data analyzer, and configured to: receive the measure of phase-amplitude coupling from the data analyzer; process the measure of phase-amplitude coupling against a criterion; and initiate an action by the implantable medical device when the measure of phase-amplitude coupling satisfies the criterion, wherein the data analyzer determines a measure of phase-amplitude coupling by being further configured to: for each detected feature in the low frequency range, assign a phase range to each of a plurality of portions of the detected feature; for each portion of the detected feature, determine an individual metric based on a count of detected features in the higher frequency range that coincide with the portion; for each assigned phase range, determine an aggregate metric based on the individual metrics determined for each portion of the detected feature having the phase range assigned thereto; and determine the measure of phase-amplitude coupling based on a plurality of determined aggregate metrics.
 2. The implantable medical device of claim 1, wherein the individual metric for a portion of the detected feature is weighted based on a characteristic of the detected feature.
 3. The implantable medical device of claim 1, wherein the individual metric for a portion of the detected feature is normalized based on a measure of the duration of the portion.
 4. The implantable medical device of claim 1, wherein the aggregate metric comprises a sum or a statistical measure of the individual metrics.
 5. The implantable medical device of claim 1, wherein the measure of phase-amplitude coupling corresponds to an index that is a difference between a maximum of the plurality of determined aggregate metrics and a minimum of the plurality of determined aggregate metrics.
 6. The implantable medical device of claim 1, wherein the selected portion of the electrographic signal corresponds to a portion of the electrographic signal that lies within a processing window having a specified duration.
 7. An implantable medical device comprising: at least one sensor configured to be implanted in or on a brain, and to sense electrical activity of the brain; a data analyzer coupled to the at least one sensor and configured to: monitor an electrographic signal corresponding to the electrical activity of the brain sensed by the at least one sensor; for a selected portion of the electrographic signal, detect features of the electrographic signal that represent oscillations in a low frequency range, and features of the electrographic signal that represent oscillations in a higher frequency range; and determine a measure of phase-amplitude coupling between oscillations in the low frequency range and oscillations in the higher frequency range based on occurrences of oscillations in the higher frequency range which coincide with one or more phases of oscillations in the low frequency range; and a therapy subsystem coupled to the data analyzer, and configured to: receive the measure of phase-amplitude coupling from the data analyzer; process the measure of phase-amplitude coupling against a criterion; and initiate an action by the implantable medical device when the measure of phase-amplitude coupling satisfies the criterion, wherein the data analyzer is configured to: detect features of the electrographic signal that represent oscillations in the low frequency range by applying the selected portion of the electrographic signal to a half wave detector programmed to detect half waves within the low frequency range, and detect features of the electrographic signal that represent oscillations in the higher frequency range by applying the selected portion of the electrographic signal to a half wave detector programmed to detect half waves within the higher frequency range.
 8. The implantable medical device of claim 7, wherein the data analyzer determines a measure of phase-amplitude coupling by being further configured to: a) for each detected half wave within the low frequency range: i) divide the detected half wave into a plurality of phase bins; ii) assign a bin number to each of the plurality of phase bins, wherein the bin number is selected from a first set of bin numbers when the detected half wave has an upward slope, and from a second set of bin numbers when the detected half wave has a downward slope; b) for each phase bin, determine an individual metric based on a number of detected half waves within the higher frequency range that coincide with the phase bin; c) for each assigned bin number, determine an aggregate metric based on the individual metrics determined for each phase bin having the bin number assigned thereto; and d) determine the measure of phase-amplitude coupling based on a plurality of determined aggregate metrics.
 9. The implantable medical device of claim 8, wherein data analyzer is further configured to designate each of the detected half waves within the low frequency range is as: a) a qualified half wave, or b) an unqualified half wave, and each of the detected half waves within the higher frequency range is as: a) a qualified half wave, or b) an unqualified half wave.
 10. The implantable medical device of claim 9, wherein the individual metric for each phase bin is either weighted or unweighted based on designations of the detected half wave within the low frequency range and the detected half waves within the higher frequency range.
 11. The implantable medical device of claim 10, wherein: the individual metric for a phase bin is weighted when the phase bin is part of an unqualified half wave within the low frequency range or when a detected half wave within the higher frequency range that coincides with the phase bin is an unqualified half wave, and the individual metric for a phase bin is when the phase bin is part of a qualified half wave within the low frequency range and when each detected half wave within the higher frequency range that coincides with the phase bin is a qualified half wave.
 12. The implantable medical device of claim 8, wherein: the aggregate metric for an assigned bin number comprises a sum or a statistical measure of the individual metrics obtained for the assigned bin number, and the determined measure of phase-amplitude coupling comprises an index that is a difference between an aggregate metric for a first bin number and an aggregate metric for a second bin number.
 13. The implantable medical device of claim 1, wherein: the low frequency range is one of: a delta frequency range of approximately 1 to 4 Hz, a theta frequency range of approximately 4 to 8 Hz, an alpha frequency range of approximately 8 to 13 Hz, and a beta frequency range of approximately 13 to 30 Hz, and the higher frequency range is one of: a theta frequency range of approximately 4 to 8 Hz, an alpha frequency range of approximately 8 to 13 Hz, a beta frequency range of approximately 13 to 30 Hz, and a gamma frequency range of approximately greater than 30 Hz.
 14. The implantable medical device of claim 1, wherein the action modulates neural activity.
 15. The implantable medical device of claim 1, wherein the action comprises one or more of: 1) a delivery of a therapy to a patient, and 2) a modification of one or more therapy parameters defining a therapy to be delivered to a patient.
 16. The implantable medical device of claim 1, wherein the criterion is a threshold and the criterion is satisfied when the measure of phase-amplitude coupling exceeds the threshold.
 17. The implantable medical device of claim 1, wherein the criterion is a threshold and the criterion is satisfied when the measure of phase-amplitude coupling is below the threshold.
 18. A method of measuring phase-amplitude coupling comprising: sensing electrical activity of a brain with at least one sensor implanted in or on the brain; monitoring with an implanted data analyzer an electrographic signal corresponding to the electrical activity of the brain sensed by the at least one sensor; for a selected portion of the electrographic signal, detecting with the implanted data analyzer features of the electrographic signal that represent oscillations in a low frequency range, and features of the electrographic signal that represent oscillations in a higher frequency range; determining with the implanted data analyzer a measure of phase-amplitude coupling between oscillations in the low frequency range and oscillations in the higher frequency range based on occurrences of oscillations in the higher frequency range which coincide with one or more phases of oscillations in the low frequency range; processing with an implanted therapy subsystem the measure of phase-amplitude coupling against a criterion; and initiating with the implanted therapy subsystem an action when the measure of phase-amplitude coupling satisfies the criterion, wherein determining a measure of phase-amplitude coupling comprises: for each detected feature in the low frequency range, assigning a phase range to each of a plurality of portions of the detected feature; for each portion of the detected feature, determining an individual metric based on a count of detected features in the higher frequency range that coincide with the portion; for each assigned phase range, determining an aggregate metric based on the individual metrics determined for each portion of the detected feature having the phase range assigned thereto; and determining the measure of phase-amplitude coupling based on a plurality of determined aggregate metrics.
 19. A method of measuring phase-amplitude coupling comprising: sensing electrical activity of a brain with at least one sensor implanted in or on the brain; monitoring with an implanted data analyzer an electrographic signal corresponding to the electrical activity of the brain sensed by the at least one sensor; for a selected portion of the electrographic signal, detecting with the implanted data analyzer features of the electrographic signal that represent oscillations in a low frequency range, and features of the electrographic signal that represent oscillations in a higher frequency range; determine with the implanted data analyzer a measure of phase-amplitude coupling between oscillations in the low frequency range and oscillations in the higher frequency range based on occurrences of oscillations in the higher frequency range which coincide with one or more phases of oscillations in the low frequency range; processing with an implanted therapy subsystem the measure of phase-amplitude coupling against a criterion; and initiating with the implanted therapy subsystem an action when the measure of phase-amplitude coupling satisfies the criterion, wherein: detecting features of the electrographic signal that represent oscillations in a low frequency range comprises applying the selected portion of the electrographic signal to a half wave detector programmed to detect half waves within the low frequency range, and detecting features of the electrographic signal that represent oscillations in a higher frequency range comprises applying the selected portion of the electrographic signal to a half wave detector programmed to detect half waves within the higher frequency range.
 20. The method of claim 19, wherein determining a measure of phase-amplitude coupling comprises: a) for each detected half wave within the low frequency range: i) dividing the detected half wave into a plurality of phase bins; ii) assigning a bin number to each of the plurality of phase bins, wherein the bin number is selected from a first set of bin numbers when the detected half wave has an upward slope, and from a second set of bin numbers when the detected half wave has a downward slope; b) for each phase bin, determining an individual metric based on a number of detected half waves within the higher frequency range that coincide with the phase bin; c) for each assigned bin number, determining an aggregate metric based on the individual metrics determined for each phase bin having the bin number assigned thereto; and d) determining the measure of phase-amplitude coupling based on a plurality of determined aggregate metrics.
 21. The method of claim 18, wherein the action modulates neural activity.
 22. The method of claim 18, wherein initiating an action comprises one or more of: 1) delivering a therapy to a patient, and 2) modifying one or more therapy parameters defining a therapy to be delivered to a patient. 